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Boehm D, Strantz C, Christoph J, Busch H, Ganslandt T, Unberath P. Data Visualization Support for Tumor Boards and Clinical Oncology: Protocol for a Scoping Review. JMIR Res Protoc 2024; 13:e53627. [PMID: 38441925 PMCID: PMC10951826 DOI: 10.2196/53627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/17/2023] [Accepted: 12/19/2023] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND Complex and expanding data sets in clinical oncology applications require flexible and interactive visualization of patient data to provide the maximum amount of information to physicians and other medical practitioners. Interdisciplinary tumor conferences in particular profit from customized tools to integrate, link, and visualize relevant data from all professions involved. OBJECTIVE The scoping review proposed in this protocol aims to identify and present currently available data visualization tools for tumor boards and related areas. The objective of the review will be to provide not only an overview of digital tools currently used in tumor board settings, but also the data included, the respective visualization solutions, and their integration into hospital processes. METHODS The planned scoping review process is based on the Arksey and O'Malley scoping study framework. The following electronic databases will be searched for articles published in English: PubMed, Web of Knowledge, and SCOPUS. Eligible articles will first undergo a deduplication step, followed by the screening of titles and abstracts. Second, a full-text screening will be used to reach the final decision about article selection. At least 2 reviewers will independently screen titles, abstracts, and full-text reports. Conflicting inclusion decisions will be resolved by a third reviewer. The remaining literature will be analyzed using a data extraction template proposed in this protocol. The template includes a variety of meta information as well as specific questions aiming to answer the research question: "What are the key features of data visualization solutions used in molecular and organ tumor boards, and how are these elements integrated and used within the clinical setting?" The findings will be compiled, charted, and presented as specified in the scoping study framework. Data for included tools may be supplemented with additional manual literature searches. The entire review process will be documented in alignment with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart. RESULTS The results of this scoping review will be reported per the expanded PRISMA-ScR guidelines. A preliminary search using PubMed, Web of Knowledge, and Scopus resulted in 1320 articles after deduplication that will be included in the further review process. We expect the results to be published during the second quarter of 2024. CONCLUSIONS Visualization is a key process in leveraging a data set's potentially available information and enabling its use in an interdisciplinary setting. The scoping review described in this protocol aims to present the status quo of visualization solutions for tumor board and clinical oncology applications and their integration into hospital processes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/53627.
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Affiliation(s)
- Dominik Boehm
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Bavarian Cancer Research Center (Bayerisches Zentrum für Krebsforschung), Erlangen, Germany
| | - Cosima Strantz
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Jan Christoph
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Junior Research Group (Bio-)medical Data Science, Faculty of Medicine, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Hauke Busch
- Group for Medical Systems Biology, Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Thomas Ganslandt
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Philipp Unberath
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- SRH Fürth University of Applied Sciences, Fürth, Germany
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2
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Scott-Boyer MP, Dufour P, Belleau F, Ongaro-Carcy R, Plessis C, Périn O, Droit A. Use of Elasticsearch-based business intelligence tools for integration and visualization of biological data. Brief Bioinform 2023; 24:bbad348. [PMID: 37798252 DOI: 10.1093/bib/bbad348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/23/2023] [Accepted: 09/14/2023] [Indexed: 10/07/2023] Open
Abstract
The emergence of massive datasets exploring the multiple levels of molecular biology has made their analysis and knowledge transfer more complex. Flexible tools to manage big biological datasets could be of great help for standardizing the usage of developed data visualizations and integration methods. Business intelligence (BI) tools have been used in many fields as exploratory tools. They have numerous connectors to link numerous data repositories with a unified graphic interface, offering an overview of data and facilitating interpretation for decision makers. BI tools could be a flexible and user-friendly way of handling molecular biological data with interactive visualizations. However, it is rather uncommon to see such tools used for the exploration of massive and complex datasets in biological fields. We believe that two main obstacles could be the reason. Firstly, we posit that the way to import data into BI tools are not compatible with biological databases. Secondly, BI tools may not be adapted to certain particularities of complex biological data, namely, the size, the variability of datasets and the availability of specialized visualizations. This paper highlights the use of five BI tools (Elastic Kibana, Siren Investigate, Microsoft Power BI, Salesforce Tableau and Apache Superset) onto which the massive data management repository engine called Elasticsearch is compatible. Four case studies will be discussed in which these BI tools were applied on biological datasets with different characteristics. We conclude that the performance of the tools depends on the complexity of the biological questions and the size of the datasets.
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Affiliation(s)
- Marie-Pier Scott-Boyer
- Centre de Recherche du CHU de Québec-Université, Laval, Université Laval, G1V 4G2, Québec, Canada
| | - Pascal Dufour
- Centre de Recherche du CHU de Québec-Université, Laval, Université Laval, G1V 4G2, Québec, Canada
| | - François Belleau
- Centre de Recherche du CHU de Québec-Université, Laval, Université Laval, G1V 4G2, Québec, Canada
| | - Regis Ongaro-Carcy
- Centre de Recherche du CHU de Québec-Université, Laval, Université Laval, G1V 4G2, Québec, Canada
- Département de Médecine Moléculaire, G1V 0A6, Québec, Canada
| | - Clément Plessis
- Centre de Recherche du CHU de Québec-Université, Laval, Université Laval, G1V 4G2, Québec, Canada
| | - Olivier Périn
- L'Oréal Advance Research, Aulnay-sous-Bois, 93600, France
| | - Arnaud Droit
- Centre de Recherche du CHU de Québec-Université, Laval, Université Laval, G1V 4G2, Québec, Canada
- Département de Médecine Moléculaire, G1V 0A6, Québec, Canada
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3
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Hurley F, Heitsch C. RNAprofiling 2.0: Enhanced Cluster Analysis of Structural Ensembles. J Mol Biol 2023; 435:168047. [PMID: 36933824 DOI: 10.1016/j.jmb.2023.168047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 03/05/2023] [Accepted: 03/09/2023] [Indexed: 03/18/2023]
Abstract
Understanding the base pairing of an RNA sequence provides insight into its molecular structure. By mining suboptimal sampling data, RNAprofiling 1.0 identifies the dominant helices in low-energy secondary structures as features, organizes them into profiles which partition the Boltzmann sample, and highlights key similarities/differences among the most informative, i.e. selected, profiles in a graphical format. Version 2.0 enhances every step of this approach. First, the featured substructures are expanded from helices to stems. Second, profile selection includes low-frequency pairings similar to featured ones. In conjunction, these updates extend the utility of the method to sequences up to length 600, as evaluated over a sizable dataset. Third, relationships are visualized in a decision tree which highlights the most important structural differences. Finally, this cluster analysis is made accessible to experimental researchers in a portable format as an interactive webpage, permitting a much greater understanding of trade-offs among different possible base pairing combinations.
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Affiliation(s)
- Forrest Hurley
- University of North Carolina at Chapel Hill, United States
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4
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Albarrak AM. Improving the Trustworthiness of Interactive Visualization Tools for Healthcare Data through a Medical Fuzzy Expert System. Diagnostics (Basel) 2023; 13:diagnostics13101733. [PMID: 37238218 DOI: 10.3390/diagnostics13101733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 05/07/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
Successful healthcare companies and illness diagnostics require data visualization. Healthcare and medical data analysis are needed to use compound information. Professionals often gather, evaluate, and monitor medical data to gauge risk, performance capability, tiredness, and adaptation to a medical diagnosis. Medical diagnosis data come from EMRs, software systems, hospital administration systems, laboratories, IoT devices, and billing and coding software. Interactive diagnosis data visualization tools enable healthcare professionals to identify trends and interpret data analytics results. Selecting the most trustworthy interactive visualization tool or application is crucial for the reliability of medical diagnosis data. Thus, this study examined the trustworthiness of interactive visualization tools for healthcare data analytics and medical diagnosis. The present study uses a scientific approach for evaluating the trustworthiness of interactive visualization tools for healthcare and medical diagnosis data and provides a novel idea and path for future healthcare experts. Our goal in this research was to make an idealness assessment of the trustworthiness impact of interactive visualization models under fuzzy conditions by using a medical fuzzy expert system based on an analytical network process and technique for ordering preference by similarity to ideal solutions. To eliminate the ambiguities that arose due to the multiple opinions of these experts and to externalize and organize information about the selection context of the interactive visualization models, the study used the proposed hybrid decision model. According to the results achieved through trustworthiness assessments of different visualization tools, BoldBI was found to be the most prioritized and trustworthy visualization tool among other alternatives. The suggested study would aid healthcare and medical professionals in interactive data visualization in identifying, selecting, prioritizing, and evaluating useful and trustworthy visualization-related characteristics, thereby leading to more accurate medical diagnosis profiles.
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Affiliation(s)
- Abdullah M Albarrak
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 13318, Saudi Arabia
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5
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Kerley CI, Nguyen TQ, Ramadass K, Cutting LE, Landman BA, Berger M. pyPheWAS Explorer: a visualization tool for exploratory analysis of phenome-disease associations. JAMIA Open 2023; 6:ooad018. [PMID: 37021295 PMCID: PMC10070037 DOI: 10.1093/jamiaopen/ooad018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 01/23/2023] [Accepted: 03/24/2023] [Indexed: 04/05/2023] Open
Abstract
Objective To enable interactive visualization of phenome-wide association studies (PheWAS) on electronic health records (EHR). Materials and Methods Current PheWAS technologies require familiarity with command-line interfaces and lack end-to-end data visualizations. pyPheWAS Explorer allows users to examine group variables, test assumptions, design PheWAS models, and evaluate results in a streamlined graphical interface. Results A cohort of attention deficit hyperactivity disorder (ADHD) subjects and matched non-ADHD controls is examined. pyPheWAS Explorer is used to build a PheWAS model including sex and deprivation index as covariates, and the Explorer's result visualization for this model reveals known ADHD comorbidities. Discussion pyPheWAS Explorer may be used to rapidly investigate potentially novel EHR associations. Broader applications include deployment for clinical experts and preliminary exploration tools for institutional EHR repositories. Conclusion pyPheWAS Explorer provides a seamless graphical interface for designing, executing, and analyzing PheWAS experiments, emphasizing exploratory analysis of regression types and covariate selection.
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Affiliation(s)
- Cailey I Kerley
- Department of Electrical & Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Tin Q Nguyen
- Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Special Education, Peabody College of Education and Human Development, Nashville, Tennessee, USA
| | - Karthik Ramadass
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Laurie E Cutting
- Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Special Education, Peabody College of Education and Human Development, Nashville, Tennessee, USA
| | - Bennett A Landman
- Department of Electrical & Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Matthew Berger
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
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6
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Muacevic A, Adler JR. Visualization Techniques in Healthcare Applications: A Narrative Review. Cureus 2022; 14:e31355. [PMID: 36514654 PMCID: PMC9741729 DOI: 10.7759/cureus.31355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/11/2022] [Indexed: 11/13/2022] Open
Abstract
Nowadays, healthcare management systems are adopting various techniques that facilitate the achievement of the goals of evidence-based medical practice. This review explores different visualization techniques and their importance in healthcare contexts. We performed a thorough search on databases such as the SLD portal, PubMed, and Google Scholar to obtain relevant studies. We selected recent articles published between 2018 and 2021 on visualization techniques in healthcare. The field of healthcare generates massive volumes of data that require visualization techniques to make them easily comprehensible and to guide their efficient presentation. Visualization in healthcare involves the effective presentation of information through graphics, images, and videos. Big data systems handle a massive amount of information and require visualization techniques to present it in a comprehensible manner. The significance of visualization techniques in healthcare is not confined to healthcare practitioners and healthcare management but encompasses all the stakeholders; patients can benefit from the visualization of his/her data for a better understanding of their condition. In short, visualization techniques have demonstrated their benefits in the healthcare sector and can be extended to the payer and the patient. They have also had a positive impact on the quality of the healthcare provided as well as patient safety.
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7
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Bertelli C, Gray KL, Woods N, Lim AC, Tilley KE, Winsor GL, Hoad GR, Roudgar A, Spencer A, Peltier J, Warren D, Raphenya AR, McArthur AG, Brinkman FSL. Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks. Microb Genom 2022; 8. [PMID: 35584003 PMCID: PMC9465072 DOI: 10.1099/mgen.0.000818] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Outbreaks of virulent and/or drug-resistant bacteria have a significant impact on human health and major economic consequences. Genomic islands (GIs; defined as clusters of genes of probable horizontal origin) are of high interest because they disproportionately encode virulence factors, some antimicrobial-resistance (AMR) genes, and other adaptations of medical or environmental interest. While microbial genome sequencing has become rapid and inexpensive, current computational methods for GI analysis are not amenable for rapid, accurate, user-friendly and scalable comparative analysis of sets of related genomes. To help fill this gap, we have developed IslandCompare, an open-source computational pipeline for GI prediction and comparison across several to hundreds of bacterial genomes. A dynamic and interactive visualization strategy displays a bacterial core-genome phylogeny, with bacterial genomes linearly displayed at the phylogenetic tree leaves. Genomes are overlaid with GI predictions and AMR determinants from the Comprehensive Antibiotic Resistance Database (CARD), and regions of similarity between the genomes are also displayed. GI predictions are performed using Sigi-HMM and IslandPath-DIMOB, the two most precise GI prediction tools based on nucleotide composition biases, as well as a novel blast-based consistency step to improve cross-genome prediction consistency. GIs across genomes sharing sequence similarity are grouped into clusters, further aiding comparative analysis and visualization of acquisition and loss of mobile GIs in specific sub-clades. IslandCompare is an open-source software that is containerized for local use, plus available via a user-friendly, web-based interface to allow direct use by bioinformaticians, biologists and clinicians (at https://islandcompare.ca).
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Affiliation(s)
- Claire Bertelli
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada.,Institute of Microbiology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Kristen L Gray
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Nolan Woods
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Adrian C Lim
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Keith E Tilley
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Geoffrey L Winsor
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Gemma R Hoad
- Research Computing Group, Simon Fraser University, Burnaby, BC, Canada
| | - Ata Roudgar
- Research Computing Group, Simon Fraser University, Burnaby, BC, Canada
| | - Adam Spencer
- Research Computing Group, Simon Fraser University, Burnaby, BC, Canada
| | - James Peltier
- Research Computing Group, Simon Fraser University, Burnaby, BC, Canada
| | - Derek Warren
- Research Computing Group, Simon Fraser University, Burnaby, BC, Canada
| | - Amogelang R Raphenya
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, ON, Canada.,Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada.,Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
| | - Andrew G McArthur
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, ON, Canada.,Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada.,Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
| | - Fiona S L Brinkman
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
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8
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Chishtie J, Bielska IA, Barrera A, Marchand JS, Imran M, Tirmizi SFA, Turcotte LA, Munce S, Shepherd J, Senthinathan A, Cepoiu-Martin M, Irvine M, Babineau J, Abudiab S, Bjelica M, Collins C, Craven BC, Guilcher S, Jeji T, Naraei P, Jaglal S. Interactive Visualization Applications in Population Health and Health Services Research: Systematic Scoping Review. J Med Internet Res 2022; 24:e27534. [PMID: 35179499 PMCID: PMC8900899 DOI: 10.2196/27534] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/27/2021] [Accepted: 10/08/2021] [Indexed: 11/15/2022] Open
Abstract
Background Simple visualizations in health research data, such as scatter plots, heat maps, and bar charts, typically present relationships between 2 variables. Interactive visualization methods allow for multiple related facets such as numerous risk factors to be studied simultaneously, leading to data insights through exploring trends and patterns from complex big health care data. The technique presents a powerful tool that can be used in combination with statistical analysis for knowledge discovery, hypothesis generation and testing, and decision support. Objective The primary objective of this scoping review is to describe and summarize the evidence of interactive visualization applications, methods, and tools being used in population health and health services research (HSR) and their subdomains in the last 15 years, from January 1, 2005, to March 30, 2019. Our secondary objective is to describe the use cases, metrics, frameworks used, settings, target audience, goals, and co-design of applications. Methods We adapted standard scoping review guidelines with a peer-reviewed search strategy: 2 independent researchers at each stage of screening and abstraction, with a third independent researcher to arbitrate conflicts and validate findings. A comprehensive abstraction platform was built to capture the data from diverse bodies of literature, primarily from the computer science and health care sectors. After screening 11,310 articles, we present findings from 56 applications from interrelated areas of population health and HSR, as well as their subdomains such as epidemiologic surveillance, health resource planning, access, and use and costs among diverse clinical and demographic populations. Results In this companion review to our earlier systematic synthesis of the literature on visual analytics applications, we present findings in 6 major themes of interactive visualization applications developed for 8 major problem categories. We found a wide application of interactive visualization methods, the major ones being epidemiologic surveillance for infectious disease, resource planning, health service monitoring and quality, and studying medication use patterns. The data sources included mostly secondary administrative and electronic medical record data. In addition, at least two-thirds of the applications involved participatory co-design approaches while introducing a distinct category, embedded research, within co-design initiatives. These applications were in response to an identified need for data-driven insights into knowledge generation and decision support. We further discuss the opportunities stemming from the use of interactive visualization methods in studying global health; inequities, including social determinants of health; and other related areas. We also allude to the challenges in the uptake of these methods. Conclusions Visualization in health has strong historical roots, with an upward trend in the use of these methods in population health and HSR. Such applications are being fast used by academic and health care agencies for knowledge discovery, hypotheses generation, and decision support. International Registered Report Identifier (IRRID) RR2-10.2196/14019
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Affiliation(s)
- Jawad Chishtie
- Rehabilitation Sciences Institute, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Center for Health Informatics, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Edmonton, AB, Canada
| | | | | | | | | | | | | | - Sarah Munce
- Rehabilitation Sciences Institute, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON, Canada.,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - John Shepherd
- Rehabilitation Sciences Institute, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Arrani Senthinathan
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | | | - Michael Irvine
- Department of Mathematics, University of British Columbia, Vancouver, BC, Canada.,British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Jessica Babineau
- Library & Information Services, University Health Network, Toronto, ON, Canada.,The Institute for Education Research, University Health Network, Toronto, ON, Canada
| | - Sally Abudiab
- Rehabilitation Sciences Institute, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Marko Bjelica
- Rehabilitation Sciences Institute, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | | | - B Catharine Craven
- Rehabilitation Sciences Institute, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,KITE, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
| | - Sara Guilcher
- Rehabilitation Sciences Institute, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.,Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Tara Jeji
- Ontario Neurotrauma Foundation, Toronto, ON, Canada
| | - Parisa Naraei
- Department of Computer Science, Ryerson University, Toronto, ON, Canada
| | - Susan Jaglal
- Rehabilitation Sciences Institute, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.,Department of Physical Therapy, University of Toronto, Toronto, ON, Canada
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9
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Sarriegi JK, Iraola AB, Álvarez Sánchez R, Graña M, Rebescher KM, Epelde G, Hopper L, Carroll J, Ianes PG, Gasperini B, Pilla F, Mattei W, Tessarolo F, Petsani D, Bamidis PD, Konstantinidis EI. COLAEVA: Visual Analytics and Data Mining Web-Based Tool for Virtual Coaching of Older Adult Populations. Sensors (Basel) 2021; 21:7991. [PMID: 34883995 PMCID: PMC8659844 DOI: 10.3390/s21237991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/15/2021] [Accepted: 11/24/2021] [Indexed: 11/16/2022]
Abstract
The global population is aging in an unprecedented manner and the challenges for improving the lives of older adults are currently both a strong priority in the political and healthcare arena. In this sense, preventive measures and telemedicine have the potential to play an important role in improving the number of healthy years older adults may experience and virtual coaching is a promising research area to support this process. This paper presents COLAEVA, an interactive web application for older adult population clustering and evolution analysis. Its objective is to support caregivers in the design, validation and refinement of coaching plans adapted to specific population groups. COLAEVA enables coaching caregivers to interactively group similar older adults based on preliminary assessment data, using AI features, and to evaluate the influence of coaching plans once the final assessment is carried out for a baseline comparison. To evaluate COLAEVA, a usability test was carried out with 9 test participants obtaining an average SUS score of 71.1. Moreover, COLAEVA is available online to use and explore.
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Affiliation(s)
- Jon Kerexeta Sarriegi
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 San Sebastián, Spain; (R.Á.S.); (K.M.R.); (G.E.)
- Biodonostia Health Research Institute, 20014 San Sebastián, Spain
- Computational Intelligence Group, Computer Science Faculty, University of the Basque Country, UPV/EHU, 20018 San Sebastián, Spain;
| | - Andoni Beristain Iraola
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 San Sebastián, Spain; (R.Á.S.); (K.M.R.); (G.E.)
- Biodonostia Health Research Institute, 20014 San Sebastián, Spain
- Computational Intelligence Group, Computer Science Faculty, University of the Basque Country, UPV/EHU, 20018 San Sebastián, Spain;
| | - Roberto Álvarez Sánchez
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 San Sebastián, Spain; (R.Á.S.); (K.M.R.); (G.E.)
- Biodonostia Health Research Institute, 20014 San Sebastián, Spain
| | - Manuel Graña
- Computational Intelligence Group, Computer Science Faculty, University of the Basque Country, UPV/EHU, 20018 San Sebastián, Spain;
| | - Kristin May Rebescher
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 San Sebastián, Spain; (R.Á.S.); (K.M.R.); (G.E.)
| | - Gorka Epelde
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 San Sebastián, Spain; (R.Á.S.); (K.M.R.); (G.E.)
- Biodonostia Health Research Institute, 20014 San Sebastián, Spain
| | - Louise Hopper
- School of Psychology, Dublin City University, Glasnevin, D09 X984 Dublin, Ireland; (L.H.); (J.C.)
| | - Joanne Carroll
- School of Psychology, Dublin City University, Glasnevin, D09 X984 Dublin, Ireland; (L.H.); (J.C.)
| | - Patrizia Gabriella Ianes
- Unità Operativa Riabilitazione Ospedaliera—Villa Rosa, Azienda Provinciale per i Servizi Sanitari di Trento, 38123 Trento, Italy; (P.G.I.); (B.G.); (F.P.)
| | - Barbara Gasperini
- Unità Operativa Riabilitazione Ospedaliera—Villa Rosa, Azienda Provinciale per i Servizi Sanitari di Trento, 38123 Trento, Italy; (P.G.I.); (B.G.); (F.P.)
| | - Francesco Pilla
- Unità Operativa Riabilitazione Ospedaliera—Villa Rosa, Azienda Provinciale per i Servizi Sanitari di Trento, 38123 Trento, Italy; (P.G.I.); (B.G.); (F.P.)
| | - Walter Mattei
- Servizio Ingegneria Clinica, Azienda Provinciale per i Servizi Sanitari di Trento, 38123 Trento, Italy;
| | - Francesco Tessarolo
- Department of Industrial Engineering, University of Trento, 38123 Trento, Italy;
| | - Despoina Petsani
- Medical Physics and Digital Innovation Lab, School of Medicine, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece; (D.P.); (P.D.B.); (E.I.K.)
| | - Panagiotis D. Bamidis
- Medical Physics and Digital Innovation Lab, School of Medicine, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece; (D.P.); (P.D.B.); (E.I.K.)
| | - Evdokimos I. Konstantinidis
- Medical Physics and Digital Innovation Lab, School of Medicine, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece; (D.P.); (P.D.B.); (E.I.K.)
- WITA SRL, 38123 Trento, Italy
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10
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Cordes J, Enzlein T, Marsching C, Hinze M, Engelhardt S, Hopf C, Wolf I. M2aia-Interactive, fast, and memory-efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data. Gigascience 2021; 10:giab049. [PMID: 34282451 PMCID: PMC8290197 DOI: 10.1093/gigascience/giab049] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/19/2021] [Accepted: 06/25/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Mass spectrometry imaging (MSI) is a label-free analysis method for resolving bio-molecules or pharmaceuticals in the spatial domain. It offers unique perspectives for the examination of entire organs or other tissue specimens. Owing to increasing capabilities of modern MSI devices, the use of 3D and multi-modal MSI becomes feasible in routine applications-resulting in hundreds of gigabytes of data. To fully leverage such MSI acquisitions, interactive tools for 3D image reconstruction, visualization, and analysis are required, which preferably should be open-source to allow scientists to develop custom extensions. FINDINGS We introduce M2aia (MSI applications for interactive analysis in MITK), a software tool providing interactive and memory-efficient data access and signal processing of multiple large MSI datasets stored in imzML format. M2aia extends MITK, a popular open-source tool in medical image processing. Besides the steps of a typical signal processing workflow, M2aia offers fast visual interaction, image segmentation, deformable 3D image reconstruction, and multi-modal registration. A unique feature is that fused data with individual mass axes can be visualized in a shared coordinate system. We demonstrate features of M2aia by reanalyzing an N-glycan mouse kidney dataset and 3D reconstruction and multi-modal image registration of a lipid and peptide dataset of a mouse brain, which we make publicly available. CONCLUSIONS To our knowledge, M2aia is the first extensible open-source application that enables a fast, user-friendly, and interactive exploration of large datasets. M2aia is applicable to a wide range of MSI analysis tasks.
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Affiliation(s)
- Jonas Cordes
- Faculty of Computer Science, Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
- Medical Faculty Mannheim, University Heidelberg, Theodor Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Thomas Enzlein
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
| | - Christian Marsching
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
| | - Marven Hinze
- Faculty of Computer Science, Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
| | - Sandy Engelhardt
- Working Group “Artificial Intelligence in Cardiovascular Medicine” (AICM), University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
| | - Carsten Hopf
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
| | - Ivo Wolf
- Faculty of Computer Science, Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
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11
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Abstract
To understand the influence of biomass flows on ecosystems, we need to characterize and quantify migrations at various spatial and temporal scales. Representing the movements of migrating birds as a fluid, we applied a flow model to bird density and velocity maps retrieved from the European weather radar network, covering almost a year. We quantified how many birds take-off, fly, and land across Western Europe to (1) track bird migration waves between nights, (2) cumulate the number of birds on the ground and (3) quantify the seasonal flow into and out of the study area through several regional transects. Our results identified several migration waves that crossed the study area in 4 days only and included up to 188 million (M) birds that took-off in a single night. In spring, we estimated that 494 M birds entered the study area, 251 M left it, and 243 M birds remained within the study area. In autumn, 314 M birds entered the study area while 858 M left it. In addition to identifying fundamental quantities, our study highlights the potential of combining interdisciplinary data and methods to elucidate the dynamics of avian migration from nightly to yearly time scales and from regional to continental spatial scales.
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Affiliation(s)
- Raphaël Nussbaumer
- Swiss Ornithological Institute, Sempach, Switzerland.,Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
| | - Silke Bauer
- Swiss Ornithological Institute, Sempach, Switzerland
| | - Lionel Benoit
- Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
| | - Grégoire Mariethoz
- Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
| | - Felix Liechti
- Swiss Ornithological Institute, Sempach, Switzerland
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12
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Dong X, Xue H, Wei C. ivTerm-An R package for interactive visualization of functional analysis results of meta-omics data. J Cell Biochem 2021; 122:1428-1434. [PMID: 34132422 DOI: 10.1002/jcb.30019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 05/21/2021] [Accepted: 05/21/2021] [Indexed: 11/09/2022]
Abstract
Interpreting functional analysis results derived from environmental samples using direct sequencing meta-omics data, including metagenomics and meta-transcriptomics data, is challenging due to their complexity. Visualization of functional analysis results can help researchers discover relevant biological insights. Despite the availability of many R packages, there lacks interactive and comprehensive graphic systems for displaying functional terms and corresponding genes in meta-omics analysis results. Here, we present ivTerm, an R-shiny package with a user-friendly graphical interface that enables users to inspect functional annotations, compare results across multiple experiments, create customized charts, and download these charts. It provides various basic and innovative chart types to visualize functional terms and involved genes. Users can also browse the description of terms obtained from the database web servers automatically. Two examples, including a metagenome analysis data for human gut and a meta-transcriptome data for coral symbiomes, are given to show the usage of ivTerm. In the end, we compared ivTerm with existing tools with similar functions, such as GOplot, ViSEAGO, and Chordomics. The tool ivTerm is convenient and efficient for biologists to gain an integrated view and develop deep insights by interactive analysis of meta-omics data. It can accelerate the procedure to develop insights from complex meta-omics data. The code for ivTerm is freely available at https://github.com/SJTU-CGM/ivTerm.
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Affiliation(s)
- Xiaorui Dong
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Hongzhang Xue
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Chaochun Wei
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
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13
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Verschaffelt P, Van Den Bossche T, Martens L, Dawyndt P, Mesuere B. Unipept Desktop: A Faster, More Powerful Metaproteomics Results Analysis Tool. J Proteome Res 2021; 20:2005-9. [PMID: 33401902 DOI: 10.1021/acs.jproteome.0c00855] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Metaproteomics has become an important research tool to study microbial systems, which has resulted in increased metaproteomics data generation. However, efficient tools for processing the acquired data have lagged behind. One widely used tool for metaproteomics data interpretation is Unipept, a web-based tool that provides, among others, interactive and insightful visualizations. Due to its web-based implementation, however, the Unipept web application is limited in the amount of data that can be analyzed. In this manuscript we therefore present Unipept Desktop, a desktop application version of Unipept that is designed to drastically increase the throughput and capacity of metaproteomics data analysis. Moreover, it provides a novel comparative analysis pipeline and improves the organization of experimental data into projects, thus addressing the growing need for more efficient and versatile analysis tools for metaproteomics data.
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14
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Bhardwaj N, Cecchetti AA, Murughiyan U, Neitch S. Analysis of Benzodiazepine Prescription Practices in Elderly Appalachians with Dementia via the Appalachian Informatics Platform: Longitudinal Study. JMIR Med Inform 2020; 8:e18389. [PMID: 32749226 PMCID: PMC7435704 DOI: 10.2196/18389] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/27/2020] [Accepted: 06/15/2020] [Indexed: 01/22/2023] Open
Abstract
Background Caring for the growing dementia population with complex health care needs in West Virginia has been challenging due to its large, sizably rural-dwelling geriatric population and limited resource availability. Objective This paper aims to illustrate the application of an informatics platform to drive dementia research and quality care through a preliminary study of benzodiazepine (BZD) prescription patterns and its effects on health care use by geriatric patients. Methods The Maier Institute Data Mart, which contains clinical and billing data on patients aged 65 years and older (N=98,970) seen within our clinics and hospital, was created. Relevant variables were analyzed to identify BZD prescription patterns and calculate related charges and emergency department (ED) use. Results Nearly one-third (4346/13,910, 31.24%) of patients with dementia received at least one BZD prescription, 20% more than those without dementia. More women than men received at least one BZD prescription. On average, patients with dementia and at least one BZD prescription sustained higher charges and visited the ED more often than those without one. Conclusions The Appalachian Informatics Platform has the potential to enhance dementia care and research through a deeper understanding of dementia, data enrichment, risk identification, and care gap analysis.
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Affiliation(s)
- Niharika Bhardwaj
- Department of Clinical and Translational Science, Joan C Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Alfred A Cecchetti
- Department of Clinical and Translational Science, Joan C Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Usha Murughiyan
- Department of Clinical and Translational Science, Joan C Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Shirley Neitch
- Department of Internal Medicine, Joan C Edwards School of Medicine, Marshall University, Huntington, WV, United States
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15
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Zurowietz M, Nattkemper TW. An Interactive Visualization for Feature Localization in Deep Neural Networks. Front Artif Intell 2020; 3:49. [PMID: 33733166 PMCID: PMC7861262 DOI: 10.3389/frai.2020.00049] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 06/15/2020] [Indexed: 11/13/2022] Open
Abstract
Deep artificial neural networks have become the go-to method for many machine learning tasks. In the field of computer vision, deep convolutional neural networks achieve state-of-the-art performance for tasks such as classification, object detection, or instance segmentation. As deep neural networks become more and more complex, their inner workings become more and more opaque, rendering them a "black box" whose decision making process is no longer comprehensible. In recent years, various methods have been presented that attempt to peek inside the black box and to visualize the inner workings of deep neural networks, with a focus on deep convolutional neural networks for computer vision. These methods can serve as a toolbox to facilitate the design and inspection of neural networks for computer vision and the interpretation of the decision making process of the network. Here, we present the new tool Interactive Feature Localization in Deep neural networks (IFeaLiD) which provides a novel visualization approach to convolutional neural network layers. The tool interprets neural network layers as multivariate feature maps and visualizes the similarity between the feature vectors of individual pixels of an input image in a heat map display. The similarity display can reveal how the input image is perceived by different layers of the network and how the perception of one particular image region compares to the perception of the remaining image. IFeaLiD runs interactively in a web browser and can process even high resolution feature maps in real time by using GPU acceleration with WebGL 2. We present examples from four computer vision datasets with feature maps from different layers of a pre-trained ResNet101. IFeaLiD is open source and available online at https://ifealid.cebitec.uni-bielefeld.de.
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Affiliation(s)
- Martin Zurowietz
- Biodata Mining Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Tim W Nattkemper
- Biodata Mining Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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16
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Bertelli C, Tilley KE, Brinkman FSL. Microbial genomic island discovery, visualization and analysis. Brief Bioinform 2020; 20:1685-1698. [PMID: 29868902 PMCID: PMC6917214 DOI: 10.1093/bib/bby042] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Revised: 04/30/2018] [Indexed: 12/27/2022] Open
Abstract
Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions.
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Affiliation(s)
- Claire Bertelli
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Keith E Tilley
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Fiona S L Brinkman
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
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17
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Elbashti M, Aswehlee A, Nguyen CT, Ella B, Naveau A. Technical Protocol for Presenting Maxillofacial Prosthetics Concepts to Dental Students using Interactive 3D Virtual Models within a Portable Document Format. J Prosthodont 2020; 29:546-549. [PMID: 32536004 DOI: 10.1111/jopr.13210] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 06/07/2020] [Accepted: 06/08/2020] [Indexed: 11/29/2022] Open
Abstract
An appropriate presentation of maxillofacial defects and their prosthetic rehabilitation concepts using traditional two-dimensional educational materials is challenging for dental students and prosthodontics residents. This technique article introduces a simple approach to visualize and communicate three-dimensional (3D) virtual models embedded into a portable document format (PDF) file for presenting maxillofacial prosthetics concepts and enhancing students' spatial ability when learning maxillofacial prosthetics. MeVisLab software was used to combine various maxillofacial models and save them as a single 3D model. Adobe Acrobat Pro DC software was used to import the 3D model and create interactive visualization PDF documents. Adobe reader software was then used to visualize the content of the PDF documents. This approach allows educators to develop PDF files with multiple 3D models for teaching maxillofacial prosthetics concepts and communicate them with their students. Students can simply open the PDF file, activate the 3D mode, and interactively manipulate the 3D models to enhance their spatial ability for learning maxillofacial prosthetics.
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Affiliation(s)
- Mahmoud Elbashti
- Department of Maxillofacial Prosthetics, Graduate School, Tokyo Medical and Dental University, Tokyo, Japan.,Laboratory of Bioengineering of Tissues (BioTis), INSERM U1026, University of Bordeaux, Bordeaux, France
| | - Amel Aswehlee
- Department of Dental Technology, University of Tripoli, Tripoli, Libya
| | - Caroline Tram Nguyen
- Department of Oral Health Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, BC, Canada.,Department of Dentistry and Oral Oncology, British Columbia Cancer, Vancouver, BC, Canada
| | - Bruno Ella
- Oral Surgery Department, School of Surgery, Bordeaux University Hospital, Bordeaux, France
| | - Adrien Naveau
- Prosthodontics Department, School of Dentistry, University of Bordeaux, Bordeaux, France
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18
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Abstract
The statistical analysis of basketball games is a fast-growing field. Certainly, basketball data are scientifically relevant because an appropriate analysis provides a great deal of information about the performance of both players and teams. The number of games played each season generates a large amount of data worth analyzing. Basketball analytics is well established in U.S. leagues. In Europe, however, it has not been duly developed. This study focuses on the top three European team competitions: the EuroLeague, the EuroCup, and the Spanish ACB (Association of Basketball Clubs, acronym in Spanish) league. Their official websites provide access to game data for anyone who is interested, but they are only represented in a static tabular form. As a consequence, it is difficult to gain any valuable insights from them. This article presents a highly useful interactive tool, created with the free statistical software R, which makes it possible to visualize and explore basketball data from a large number of seasons. We will demonstrate its core functionality. An accompanying R package is presented in the Supplementary Data.
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Affiliation(s)
- Guillermo Vinué
- Faculty of Mathematics, University of Valencia, Valencia, Spain
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19
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Gu J, Andreopoulos S, Jenkinson J, Ng DP. Rethinking enzyme kinetics: Designing and developing a biomolecular interactive tutorial (BIOMINT) learning tool for undergraduate students. Biochem Mol Biol Educ 2020; 48:74-79. [PMID: 31532881 DOI: 10.1002/bmb.21302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 08/22/2019] [Accepted: 08/30/2019] [Indexed: 06/10/2023]
Abstract
Enzyme kinetics is the study of enzymatic catalytic rates in biochemical reactions. This topic is commonly taught to life science students in introductory biochemistry courses during their undergraduate education. Unlike most other biochemistry topics, which focus on visual structures of biomolecules and their processes, enzyme kinetics is explained primarily through abstract mathematical and two-dimensional graphical plots. However, these abstract/symbolic representations often make it difficult for students to relate the kinetic parameters to the underlying molecular system that is being described. In this article, we present the design and development of a web-based multimedia interactive learning tool, biomolecular interactive tutorials (BIOMINT) to help students better bridge the relationships between these abstract mathematical models and the molecular behaviors, interactions, and dynamics that produce kinetic phenomena. This learning tool can be accessed at https://bit.ly/biomint. © 2019 International Union of Biochemistry and Molecular Biology, 48(1):74-79, 2020.
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Affiliation(s)
- Jerry Gu
- Institute of Medical Science, University of Toronto, M5A 1A8, Toronto, Ontario, Canada
| | | | - Jodie Jenkinson
- Institute of Medical Science, University of Toronto, M5A 1A8, Toronto, Ontario, Canada
- Department of Biology, University of Toronto Mississauga, L5L 1C6, Mississauga, Ontario, Canada
| | - Derek P Ng
- Institute of Medical Science, University of Toronto, M5A 1A8, Toronto, Ontario, Canada
- Department of Biology, University of Toronto Mississauga, L5L 1C6, Mississauga, Ontario, Canada
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20
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Kiar G, Brown ST, Glatard T, Evans AC. A Serverless Tool for Platform Agnostic Computational Experiment Management. Front Neuroinform 2019; 13:12. [PMID: 30890927 PMCID: PMC6411646 DOI: 10.3389/fninf.2019.00012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 02/15/2019] [Indexed: 01/22/2023] Open
Abstract
Neuroscience has been carried into the domain of big data and high performance computing (HPC) on the backs of initiatives in data collection and an increasingly compute-intensive tools. While managing HPC experiments requires considerable technical acumen, platforms, and standards have been developed to ease this burden on scientists. While web-portals make resources widely accessible, data organizations such as the Brain Imaging Data Structure and tool description languages such as Boutiques provide researchers with a foothold to tackle these problems using their own datasets, pipelines, and environments. While these standards lower the barrier to adoption of HPC and cloud systems for neuroscience applications, they still require the consolidation of disparate domain-specific knowledge. We present Clowdr, a lightweight tool to launch experiments on HPC systems and clouds, record rich execution records, and enable the accessible sharing and re-launch of experimental summaries and results. Clowdr uniquely sits between web platforms and bare-metal applications for experiment management by preserving the flexibility of do-it-yourself solutions while providing a low barrier for developing, deploying and disseminating neuroscientific analysis.
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Affiliation(s)
- Gregory Kiar
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Shawn T. Brown
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Tristan Glatard
- Department of Computer Science, Concordia University, Montreal, QC, Canada
| | - Alan C. Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
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21
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Bellei EA, Biduski D, Lisboa HRK, De Marchi ACB. Development and Assessment of a Mobile Health Application for Monitoring the Linkage Among Treatment Factors of Type 1 Diabetes Mellitus. Telemed J E Health 2019; 26:205-217. [PMID: 30724717 DOI: 10.1089/tmj.2018.0329] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Background: In the daily routine of type 1 diabetes mellitus (T1DM), the patients deal with many data and consider many variables to perform actions, decisions, and regimen adjustments. There is a need to apply filtering techniques to extract relevant information and provide appropriate data visualization methods to assist in clinical tasks and decision making. Objective: To present Soins DM, a mobile health tool, for monitoring the linkage among treatment factors of T1DM with an interactive data visualization approach. Methods: First, we performed a literature review, a commercial search, and ideation. Next, we created a prototype and an online survey for its feedback, with participation of 76 individuals. Afterward, the mobile app and its website version were built. Eventually, we conducted a pilot experiment with 4 patients, an online experiment for satisfaction assessment with 97 patients, and an online assessment by 9 health professionals. Results: Prototyping and feedback facilitated the design refinement. Soins DM enables the recording of data from routines of glycemia, insulin applications, meals, and physical exercises. From these logs, the app builds two different ways of interactive data visualization, a timeline and an integrated chart, providing personalized feedback on bad glycemia with its possible causes. The assessments revealed overall satisfaction with the app's characteristics. Conclusions: Soins DM is a novel application with interactive visualization and personalized feedback for easy identification of the linkage among treatment factors of T1DM. The test scenario with patients and health professionals indicates Soins DM as a useful and reliable tool.
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Affiliation(s)
- Ericles Andrei Bellei
- Graduate Program in Applied Computing, Institute of Exact Sciences and Geosciences, University of Passo Fundo, Passo Fundo, Brazil
| | - Daiana Biduski
- Graduate Program in Applied Computing, Institute of Exact Sciences and Geosciences, University of Passo Fundo, Passo Fundo, Brazil
| | - Hugo Roberto Kurtz Lisboa
- IMED Medical School, Passo Fundo, Brazil.,Teaching Hospital, São Vicente de Paulo's Hospital, Passo Fundo, Brazil
| | - Ana Carolina Bertoletti De Marchi
- Graduate Program in Applied Computing, Institute of Exact Sciences and Geosciences, University of Passo Fundo, Passo Fundo, Brazil.,Graduate Program in Human Aging, College of Physical Education and Physiotherapy, University of Passo Fundo, Passo Fundo, Brazil
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22
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Liu X, Chang C, Han M, Yin R, Zhan Y, Li C, Ge C, Yu M, Yang X. PPIExp: A Web-Based Platform for Integration and Visualization of Protein-Protein Interaction Data and Spatiotemporal Proteomics Data. J Proteome Res 2018; 18:633-641. [PMID: 30565464 DOI: 10.1021/acs.jproteome.8b00713] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Integrating spatiotemporal proteomics data with protein-protein interaction (PPI) data can help researchers make an in-depth exploration of their proteins of interest in a dynamic manner. However, there is still a lack of proper tools for the biologists who usually have few programming skills to construct a PPI network for a protein list, visualize active PPI subnetworks, and then select key nodes for further study. We propose a web-based platform named PPIExp that can automatically construct a PPI network, perform clustering analysis according to protein abundances, and perform functional enrichment analysis. More importantly, it provides multiple effective visualization interfaces, such as the interface to display the PPI network map, the interface to display a dendrogram and heatmap for the clustering result, and the interface to display the expression pattern of a selected protein. To visualize the active PPI subnetworks in specific space or time, it provides buttons to highlight the differentially expressed proteins under each condition on the network map. Additionally, to help researchers determine which proteins are worth further attention, PPIExp provides extensive one-click interactive operations to map node centrality measures to node size on the network and highlight three types of proteins, that is, the proteins in an enriched functional term, the coexpressed proteins selected from the dendgrogram and heatmap, and the proteins input by users. PPIExp is available at http://www.fgvis.com/expressvis/PPIExp .
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Affiliation(s)
- Xian Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Cheng Chang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Mingfei Han
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Ronghua Yin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Yiqun Zhan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Changyan Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Changhui Ge
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Miao Yu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Xiaoming Yang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
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23
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Senk J, Carde C, Hagen E, Kuhlen TW, Diesmann M, Weyers B. VIOLA-A Multi-Purpose and Web-Based Visualization Tool for Neuronal-Network Simulation Output. Front Neuroinform 2018; 12:75. [PMID: 30467469 PMCID: PMC6236002 DOI: 10.3389/fninf.2018.00075] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 10/10/2018] [Indexed: 11/13/2022] Open
Abstract
Neuronal network models and corresponding computer simulations are invaluable tools to aid the interpretation of the relationship between neuron properties, connectivity, and measured activity in cortical tissue. Spatiotemporal patterns of activity propagating across the cortical surface as observed experimentally can for example be described by neuronal network models with layered geometry and distance-dependent connectivity. In order to cover the surface area captured by today's experimental techniques and to achieve sufficient self-consistency, such models contain millions of nerve cells. The interpretation of the resulting stream of multi-modal and multi-dimensional simulation data calls for integrating interactive visualization steps into existing simulation-analysis workflows. Here, we present a set of interactive visualization concepts called views for the visual analysis of activity data in topological network models, and a corresponding reference implementation VIOLA (VIsualization Of Layer Activity). The software is a lightweight, open-source, web-based, and platform-independent application combining and adapting modern interactive visualization paradigms, such as coordinated multiple views, for massively parallel neurophysiological data. For a use-case demonstration we consider spiking activity data of a two-population, layered point-neuron network model incorporating distance-dependent connectivity subject to a spatially confined excitation originating from an external population. With the multiple coordinated views, an explorative and qualitative assessment of the spatiotemporal features of neuronal activity can be performed upfront of a detailed quantitative data analysis of specific aspects of the data. Interactive multi-view analysis therefore assists existing data analysis workflows. Furthermore, ongoing efforts including the European Human Brain Project aim at providing online user portals for integrated model development, simulation, analysis, and provenance tracking, wherein interactive visual analysis tools are one component. Browser-compatible, web-technology based solutions are therefore required. Within this scope, with VIOLA we provide a first prototype.
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Affiliation(s)
- Johanna Senk
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
| | - Corto Carde
- Visual Computing Institute, RWTH Aachen University, Aachen, Germany
- JARA - High-Performance Computing, Aachen, Germany
- IMT Atlantique Bretagne-Pays de la Loire, Brest, France
| | - Espen Hagen
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Physics, University of Oslo, Oslo, Norway
| | - Torsten W. Kuhlen
- Visual Computing Institute, RWTH Aachen University, Aachen, Germany
- JARA - High-Performance Computing, Aachen, Germany
| | - Markus Diesmann
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
- Department of Physics, Faculty 1, RWTH Aachen University, Aachen, Germany
| | - Benjamin Weyers
- Visual Computing Institute, RWTH Aachen University, Aachen, Germany
- JARA - High-Performance Computing, Aachen, Germany
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24
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Hussain W, Campbell M, Walia H, Morota G. ShinyAIM: Shiny-based application of interactive Manhattan plots for longitudinal genome-wide association studies. Plant Direct 2018; 2:e00091. [PMID: 31245691 PMCID: PMC6508828 DOI: 10.1002/pld3.91] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 10/06/2018] [Accepted: 10/08/2018] [Indexed: 06/09/2023]
Abstract
Owning to advancements in sensor-based, non-destructive phenotyping platforms, researchers are increasingly collecting data with higher temporal resolution. These phenotypes collected over several time points are cataloged as longitudinal traits and used for genome-wide association studies (GWAS). Longitudinal GWAS typically yield a large number of output files, posing a significant challenge to data interpretation and visualization. Efficient, dynamic, and integrative data visualization tools are essential for the interpretation of longitudinal GWAS results for biologists; however, these tools are not widely available to the community. We have developed a flexible and user-friendly Shiny-based online application, ShinyAIM, to dynamically view and interpret temporal GWAS results. The main features of the application include (a) interactive Manhattan plots for single time points, (b) a grid plot to view Manhattan plots for all time points simultaneously, (c) dynamic scatter plots for p-value-filtered selected markers to investigate co-localized genomic regions across time points, (d) and interactive phenotypic data visualization to capture variation and trends in phenotypes. The application is written entirely in the R language and can be used with limited programming experience. ShinyAIM is deployed online as a Shiny web server application at https://chikudaisei.shinyapps.io/shinyaim/, enabling easy access for users without installation. The application can also be launched on a local machine in RStudio.
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Affiliation(s)
- Waseem Hussain
- Department of Animal ScienceUniversity of Nebraska‐LincolnLincolnNebraska
- Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNebraska
| | - Malachy Campbell
- Department of Animal ScienceUniversity of Nebraska‐LincolnLincolnNebraska
- Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNebraska
| | - Harkamal Walia
- Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNebraska
| | - Gota Morota
- Department of Animal ScienceUniversity of Nebraska‐LincolnLincolnNebraska
- Department of Animal and Poultry SciencesVirginia Polytechnic Institute and State UniversityBlacksburgVirginia
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25
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Abstract
Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data. The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at
https://baderlab.github.io/scClustViz/.
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Affiliation(s)
- Brendan T Innes
- Molecular Genetics, University of Toronto, Toronto, Ontario, M5S3E1, Canada.,The Donnelly Centre, University of Toronto, Toronto, Ontario, M5S3E1, Canada
| | - Gary D Bader
- Molecular Genetics, University of Toronto, Toronto, Ontario, M5S3E1, Canada.,The Donnelly Centre, University of Toronto, Toronto, Ontario, M5S3E1, Canada
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26
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Abstract
Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data. The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.
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Affiliation(s)
- Brendan T Innes
- Molecular Genetics, University of Toronto, Toronto, Ontario, M5S3E1, Canada.,The Donnelly Centre, University of Toronto, Toronto, Ontario, M5S3E1, Canada
| | - Gary D Bader
- Molecular Genetics, University of Toronto, Toronto, Ontario, M5S3E1, Canada.,The Donnelly Centre, University of Toronto, Toronto, Ontario, M5S3E1, Canada
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27
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Abstract
Similarity and distance matrices are general data structures that describe reciprocal relationships between the objects within a given dataset. Commonly used methods for representation of these matrices include heatmaps, hierarchical trees, dimensionality reduction, and various types of networks. However, despite a well-developed foundation for the visualization of such representations, the challenge of creating an interactive view that would allow for quick data navigation and interpretation remains largely unaddressed. This problem becomes especially evident for large matrices with hundreds or thousands objects. In this work, we present a web-based platform for the interactive analysis of large (dis-)similarity matrices. It consists of four major interconnected and synchronized components: a zoomable heatmap, interactive hierarchical tree, scalable circular relationship diagram, and 3D multi-dimensional scaling (MDS) scatterplot. We demonstrate the use of the platform for the analysis of amino acid covariance data in proteins as part of our previously developed CoeViz tool. The web-platform enables quick and focused analysis of protein features, such as structural domains and functional sites.
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Affiliation(s)
- Frazier N. Baker
- Department of Electrical Engineering and Computing Systems, University of Cincinnati, Cincinnati, OH 45221, USA
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Aleksey Porollo
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
- Correspondence: ; Tel.: +1-513-803-5489
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28
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Klein T, Autin L, Kozlikova B, Goodsell DS, Olson A, Groller ME, Viola I. Instant Construction and Visualization of Crowded Biological Environments. IEEE Trans Vis Comput Graph 2018; 24:862-872. [PMID: 28866533 PMCID: PMC5746312 DOI: 10.1109/tvcg.2017.2744258] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We present the first approach to integrative structural modeling of the biological mesoscale within an interactive visual environment. These complex models can comprise up to millions of molecules with defined atomic structures, locations, and interactions. Their construction has previously been attempted only within a non-visual and non-interactive environment. Our solution unites the modeling and visualization aspect, enabling interactive construction of atomic resolution mesoscale models of large portions of a cell. We present a novel set of GPU algorithms that build the basis for the rapid construction of complex biological structures. These structures consist of multiple membrane-enclosed compartments including both soluble molecules and fibrous structures. The compartments are defined using volume voxelization of triangulated meshes. For membranes, we present an extension of the Wang Tile concept that populates the bilayer with individual lipids. Soluble molecules are populated within compartments distributed according to a Halton sequence. Fibrous structures, such as RNA or actin filaments, are created by self-avoiding random walks. Resulting overlaps of molecules are resolved by a forced-based system. Our approach opens new possibilities to the world of interactive construction of cellular compartments. We demonstrate its effectiveness by showcasing scenes of different scale and complexity that comprise blood plasma, mycoplasma, and HIV.
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29
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Wang L, Yang Q, Jaimes A, Wang T, Strobelt H, Chen J, Sliz P. MightyScreen: An Open-Source Visualization Application for Screening Data Analysis. SLAS Discov 2017; 23:218-223. [PMID: 28937848 DOI: 10.1177/2472555217731983] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Screening is a methodology widely used in biological and biomedical research. There are numerous visualization methods to validate screening data quality but very few visualization applications capable of hit selection. Here, we present MightyScreen ( mightyscreen.net ), a novel web-based application designed for visual data evaluation as well as visual hit selection. We believe MightyScreen is an intuitive and interactive addition to conventional hit selection methods. We also provide study cases showing how MightyScreen is used to visually explore screening data and make hit selections.
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Affiliation(s)
- Longfei Wang
- 1 Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Qin Yang
- 1 Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Adriana Jaimes
- 1 Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Tianyu Wang
- 2 Department of Physiology and Biophysics, University of California, Irvine, CA, USA
| | - Hendrik Strobelt
- 3 School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Jenny Chen
- 4 Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Piotr Sliz
- 1 Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
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30
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Bown JL, Shovman M, Robertson P, Boiko A, Goltsov A, Mullen P, Harrison DJ. A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT. Oncotarget 2017; 8:29657-67. [PMID: 27302920 DOI: 10.18632/oncotarget.8747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Accepted: 03/31/2016] [Indexed: 12/22/2022] Open
Abstract
Targeted cancer therapy aims to disrupt aberrant cellular signalling pathways. Biomarkers are surrogates of pathway state, but there is limited success in translating candidate biomarkers to clinical practice due to the intrinsic complexity of pathway networks. Systems biology approaches afford better understanding of complex, dynamical interactions in signalling pathways targeted by anticancer drugs. However, adoption of dynamical modelling by clinicians and biologists is impeded by model inaccessibility. Drawing on computer games technology, we present a novel visualization toolkit, SiViT, that converts systems biology models of cancer cell signalling into interactive simulations that can be used without specialist computational expertise. SiViT allows clinicians and biologists to directly introduce for example loss of function mutations and specific inhibitors. SiViT animates the effects of these introductions on pathway dynamics, suggesting further experiments and assessing candidate biomarker effectiveness. In a systems biology model of Her2 signalling we experimentally validated predictions using SiViT, revealing the dynamics of biomarkers of drug resistance and highlighting the role of pathway crosstalk. No model is ever complete: the iteration of real data and simulation facilitates continued evolution of more accurate, useful models. SiViT will make accessible libraries of models to support preclinical research, combinatorial strategy design and biomarker discovery.
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31
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Bernal-Rusiel JL, Rannou N, Gollub RL, Pieper S, Murphy S, Robertson R, Grant PE, Pienaar R. Reusable Client-Side JavaScript Modules for Immersive Web-Based Real-Time Collaborative Neuroimage Visualization. Front Neuroinform 2017; 11:32. [PMID: 28507515 PMCID: PMC5410600 DOI: 10.3389/fninf.2017.00032] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 04/13/2017] [Indexed: 11/27/2022] Open
Abstract
In this paper we present a web-based software solution to the problem of implementing real-time collaborative neuroimage visualization. In both clinical and research settings, simple and powerful access to imaging technologies across multiple devices is becoming increasingly useful. Prior technical solutions have used a server-side rendering and push-to-client model wherein only the server has the full image dataset. We propose a rich client solution in which each client has all the data and uses the Google Drive Realtime API for state synchronization. We have developed a small set of reusable client-side object-oriented JavaScript modules that make use of the XTK toolkit, a popular open-source JavaScript library also developed by our team, for the in-browser rendering and visualization of brain image volumes. Efficient realtime communication among the remote instances is achieved by using just a small JSON object, comprising a representation of the XTK image renderers' state, as the Google Drive Realtime collaborative data model. The developed open-source JavaScript modules have already been instantiated in a web-app called MedView, a distributed collaborative neuroimage visualization application that is delivered to the users over the web without requiring the installation of any extra software or browser plugin. This responsive application allows multiple physically distant physicians or researchers to cooperate in real time to reach a diagnosis or scientific conclusion. It also serves as a proof of concept for the capabilities of the presented technological solution.
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Affiliation(s)
- Jorge L Bernal-Rusiel
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's HospitalBoston, MA, USA
| | | | - Randy L Gollub
- Department of Radiology, Massachusetts General HospitalBoston, MA, USA.,Department of Psychiatry, Massachusetts General HospitalBoston, MA, USA.,Harvard Medical SchoolBoston, MA, USA
| | - Steve Pieper
- Isomics Inc.Cambridge, MA, USA.,Surgical Planning Laboratory, Brigham and Women's HospitalBoston, MA, USA
| | - Shawn Murphy
- Harvard Medical SchoolBoston, MA, USA.,Department of Neurology, Massachusetts General HospitalBoston, MA, USA.,Laboratory of Computer Science, Massachusetts General HospitalBoston, MA, USA
| | - Richard Robertson
- Harvard Medical SchoolBoston, MA, USA.,Department of Radiology, Boston Children's HospitalBoston, MA, USA
| | - Patricia E Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's HospitalBoston, MA, USA.,Harvard Medical SchoolBoston, MA, USA.,Department of Radiology, Boston Children's HospitalBoston, MA, USA
| | - Rudolph Pienaar
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's HospitalBoston, MA, USA.,Harvard Medical SchoolBoston, MA, USA.,Department of Radiology, Boston Children's HospitalBoston, MA, USA
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32
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Koeva M, Luleva M, Maldjanski P. Integrating Spherical Panoramas and Maps for Visualization of Cultural Heritage Objects Using Virtual Reality Technology. Sensors (Basel) 2017; 17:s17040829. [PMID: 28398230 PMCID: PMC5422190 DOI: 10.3390/s17040829] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Revised: 03/30/2017] [Accepted: 04/07/2017] [Indexed: 11/23/2022]
Abstract
Development and virtual representation of 3D models of Cultural Heritage (CH) objects has triggered great interest over the past decade. The main reason for this is the rapid development in the fields of photogrammetry and remote sensing, laser scanning, and computer vision. The advantages of using 3D models for restoration, preservation, and documentation of valuable historical and architectural objects have been numerously demonstrated by scientists in the field. Moreover, 3D model visualization in virtual reality has been recognized as an efficient, fast, and easy way of representing a variety of objects worldwide for present-day users, who have stringent requirements and high expectations. However, the main focus of recent research is the visual, geometric, and textural characteristics of a single concrete object, while integration of large numbers of models with additional information—such as historical overview, detailed description, and location—are missing. Such integrated information can be beneficial, not only for tourism but also for accurate documentation. For that reason, we demonstrate in this paper an integration of high-resolution spherical panoramas, a variety of maps, GNSS, sound, video, and text information for representation of numerous cultural heritage objects. These are then displayed in a web-based portal with an intuitive interface. The users have the opportunity to choose freely from the provided information, and decide for themselves what is interesting to visit. Based on the created web application, we provide suggestions and guidelines for similar studies. We selected objects, which are located in Bulgaria—a country with thousands of years of history and cultural heritage dating back to ancient civilizations. The methods used in this research are applicable for any type of spherical or cylindrical images and can be easily followed and applied in various domains. After a visual and metric assessment of the panoramas and the evaluation of the web-portal, we conclude that this novel approach is a very effective, fast, informative, and accurate way to present, disseminate, and document cultural heritage objects.
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Affiliation(s)
- Mila Koeva
- Faculty of Geo-information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands.
| | - Mila Luleva
- SoilCares Research BV, 6709 PA Wageningen, The Netherlands.
| | - Plamen Maldjanski
- University of Architecture Civil Engineering and Geodesy, Sofia 1164, Bulgaria.
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33
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Nowke C, Zielasko D, Weyers B, Peyser A, Hentschel B, Kuhlen TW. Integrating Visualizations into Modeling NEST Simulations. Front Neuroinform 2015; 9:29. [PMID: 26733860 PMCID: PMC4681776 DOI: 10.3389/fninf.2015.00029] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Accepted: 11/26/2015] [Indexed: 11/13/2022] Open
Abstract
Modeling large-scale spiking neural networks showing realistic biological behavior in their dynamics is a complex and tedious task. Since these networks consist of millions of interconnected neurons, their simulation produces an immense amount of data. In recent years it has become possible to simulate even larger networks. However, solutions to assist researchers in understanding the simulation's complex emergent behavior by means of visualization are still lacking. While developing tools to partially fill this gap, we encountered the challenge to integrate these tools easily into the neuroscientists' daily workflow. To understand what makes this so challenging, we looked into the workflows of our collaborators and analyzed how they use the visualizations to solve their daily problems. We identified two major issues: first, the analysis process can rapidly change focus which requires to switch the visualization tool that assists in the current problem domain. Second, because of the heterogeneous data that results from simulations, researchers want to relate data to investigate these effectively. Since a monolithic application model, processing and visualizing all data modalities and reflecting all combinations of possible workflows in a holistic way, is most likely impossible to develop and to maintain, a software architecture that offers specialized visualization tools that run simultaneously and can be linked together to reflect the current workflow, is a more feasible approach. To this end, we have developed a software architecture that allows neuroscientists to integrate visualization tools more closely into the modeling tasks. In addition, it forms the basis for semantic linking of different visualizations to reflect the current workflow. In this paper, we present this architecture and substantiate the usefulness of our approach by common use cases we encountered in our collaborative work.
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Affiliation(s)
- Christian Nowke
- Visual Computing Institute, RWTH Aachen University, Jülich Aachen Research Alliance - High-Performance Computing Aachen, Germany
| | - Daniel Zielasko
- Visual Computing Institute, RWTH Aachen University, Jülich Aachen Research Alliance - High-Performance Computing Aachen, Germany
| | - Benjamin Weyers
- Visual Computing Institute, RWTH Aachen University, Jülich Aachen Research Alliance - High-Performance Computing Aachen, Germany
| | - Alexander Peyser
- Simulation Lab Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Forschungszentrum Jülich GmbH Jülich, Germany
| | - Bernd Hentschel
- Visual Computing Institute, RWTH Aachen University, Jülich Aachen Research Alliance - High-Performance Computing Aachen, Germany
| | - Torsten W Kuhlen
- Visual Computing Institute, RWTH Aachen University, Jülich Aachen Research Alliance - High-Performance Computing Aachen, Germany
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34
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Nguyen QV, Nelmes G, Huang ML, Simoff S, Catchpoole D. Interactive Visualization for Patient-to-Patient Comparison. Genomics Inform 2014; 12:21-34. [PMID: 24748858 PMCID: PMC3990763 DOI: 10.5808/gi.2014.12.1.21] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2013] [Revised: 02/19/2014] [Accepted: 02/20/2014] [Indexed: 12/20/2022] Open
Abstract
A visual analysis approach and the developed supporting technology provide a comprehensive solution for analyzing large and complex integrated genomic and biomedical data. This paper presents a methodology that is implemented as an interactive visual analysis technology for extracting knowledge from complex genetic and clinical data and then visualizing it in a meaningful and interpretable way. By synergizing the domain knowledge into development and analysis processes, we have developed a comprehensive tool that supports a seamless patient-to-patient analysis, from an overview of the patient population in the similarity space to the detailed views of genes. The system consists of multiple components enabling the complete analysis process, including data mining, interactive visualization, analytical views, and gene comparison. We demonstrate our approach with medical scientists on a case study of childhood cancer patients on how they use the tool to confirm existing hypotheses and to discover new scientific insights.
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Affiliation(s)
- Quang Vinh Nguyen
- MARCS Institute & School of Computing, Engineering and Mathematics, University of Western Sydney, South Penrith DC, NSW 1979, Australia
| | - Guy Nelmes
- The Kids Research Institute, The Children's Hospital at Westmead, Westmead, NSW 2145, Australia
| | - Mao Lin Huang
- School of Software, Faculty of Engineering & IT, University of Technology, Sydney, NSW 2007, Australia
| | - Simeon Simoff
- MARCS Institute & School of Computing, Engineering and Mathematics, University of Western Sydney, South Penrith DC, NSW 1979, Australia
| | - Daniel Catchpoole
- The Kids Research Institute, The Children's Hospital at Westmead, Westmead, NSW 2145, Australia
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35
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Rosen P, Popescu V. Simplification of Node Position Data ;for Interactive Visualization of Dynamic Data Sets. IEEE Trans Vis Comput Graph 2012; 18:1537-1548. [PMID: 22025753 PMCID: PMC3411892 DOI: 10.1109/tvcg.2011.268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
We propose to aid the interactive visualization of time-varying spatial data sets by simplifying node position data over the entire simulation as opposed to over individual states. Our approach is based on two observations. The first observation is that the trajectory of some nodes can be approximated well without recording the position of the node for every state. The second observation is that there are groups of nodes whose motion from one state to the next can be approximated well with a single transformation. We present data set simplification techniques that take advantage of this node data redundancy. Our techniques are general, supporting many types of simulations, they achieve good compression factors, and they allow rigorous control of the maximum node position approximation error. We demonstrate our approach in the context of finite element analysis data, of liquid flow simulation data, and of fusion simulation data.
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Affiliation(s)
- Paul Rosen
- Scientific Computing and Imaging Institute, The University of Utah, Salt Lake City, UT 84112
| | - Voicu Popescu
- Department of Computer Science, Purdue University, West Lafayette, IN 47907
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