The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research

BACKGROUND

As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs.

OBJECTIVE

We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics.

METHODS

We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base.

RESULTS

AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones.

CONCLUSIONS

AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

Ensemble pretrained language models to extract biomedical knowledge from literature

OBJECTIVES

The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking.

MATERIALS AND METHODS

For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and taxonomy names and annotated 70 more abstracts as additional corpus. We further finetuned the T0pp model, with 11 billion parameters, to boost the performance on relation extraction and leveraged entites' location information (eg, title, background) to enhance novelty prediction performance in relation extraction (RE).

RESULTS

Our pioneering NLP system designed for this challenge secured first place in Phase I-NER and second place in Phase II-relation extraction and novelty prediction, outpacing over 200 teams. We tested OpenAI ChatGPT 3.5 and ChatGPT 4 in a Zero-Shot setting using the same test set, revealing that our finetuned model considerably surpasses these broad-spectrum large language models.

DISCUSSION AND CONCLUSION

Our outcomes depict a robust NLP system excelling in NER and RE across various biomedical entities, emphasizing that task-specific models remain superior to generic large ones. Such insights are valuable for endeavors like knowledge graph development and hypothesis formulation in biomedical research.

Symbolic Artificial Intelligence to Diagnose Tuberculosis Using Ontology

Pulmonary Tuberculosis (PTB) is an infectious disease caused by a bacterium called Mycobacterium tuberculosis. This paper aims to create Symbolic Artificial Intelligence (SAI) system to diagnose PTB using clinical and paraclinical data. Usually, the automatic PTB diagnosis is based on either microbiological tests or lung X-rays. It is challenging to identify PTB accurately due to similarities with other diseases in the lungs. X-ray alone is not sufficient to diagnose PTB. Therefore, it is crucial to implement a system that can diagnose based on all paraclinical data. Thus, we propose in this paper a new PTB ontology that stores all paraclinical tests and clinical symptoms. Our SAI system includes domain ontology and a knowledge base with performance indicators and proposes a solution to diagnose current and future PTB also abnormal patients. Our approach is based on a real database of more than four years from our collaborators at Pondicherry hospital in India.

Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression (Wheeler et al., 2015). Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.

NIAGADS Alzheimer's GenomicsDB: A resource for exploring Alzheimer's disease genetic and genomic knowledge

INTRODUCTION

The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site Alzheimer's Genomics Database (GenomicsDB) is a public knowledge base of Alzheimer's disease (AD) genetic datasets and genomic annotations.

METHODS

GenomicsDB uses a custom systems architecture to adopt and enforce rigorous standards that facilitate harmonization of AD-relevant genome-wide association study summary statistics datasets with functional annotations, including over 230 million annotated variants from the AD Sequencing Project.

RESULTS

GenomicsDB generates interactive reports compiled from the harmonized datasets and annotations. These reports contextualize AD-risk associations in a broader functional genomic setting and summarize them in the context of functionally annotated genes and variants.

DISCUSSION

Created to make AD-genetics knowledge more accessible to AD researchers, the GenomicsDB is designed to guide users unfamiliar with genetic data in not only exploring but also interpreting this ever-growing volume of data. Scalable and interoperable with other genomics resources using data technology standards, the GenomicsDB can serve as a central hub for research and data analysis on AD and related dementias.

HIGHLIGHTS

The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) offers to the public a unique, disease-centric collection of AD-relevant GWAS summary statistics datasets. Interpreting these data is challenging and requires significant bioinformatics expertise to standardize datasets and harmonize them with functional annotations on genome-wide scales. The NIAGADS Alzheimer's GenomicsDB helps overcome these challenges by providing a user-friendly public knowledge base for AD-relevant genetics that shares harmonized, annotated summary statistics datasets from the NIAGADS repository in an interpretable, easily searchable format.

Sentiment interpretability analysis on Chinese texts employing multi-task and knowledge base

With the rapid development of deep learning techniques, the applications have become increasingly widespread in various domains. However, traditional deep learning methods are often referred to as "black box" models with low interpretability of their results, posing challenges for their application in certain critical domains. In this study, we propose a comprehensive method for the interpretability analysis of sentiment models. The proposed method encompasses two main aspects: attention-based analysis and external knowledge integration. First, we train the model within sentiment classification and generation tasks to capture attention scores from multiple perspectives. This multi-angle approach reduces bias and provides a more comprehensive understanding of the underlying sentiment. Second, we incorporate an external knowledge base to improve evidence extraction. By leveraging character scores, we retrieve complete sentiment evidence phrases, addressing the challenge of incomplete evidence extraction in Chinese texts. Experimental results on a sentiment interpretability evaluation dataset demonstrate the effectiveness of our method. We observe a notable increase in accuracy by 1.3%, Macro-F1 by 13%, and MAP by 23%. Overall, our approach offers a robust solution for enhancing the interpretability of sentiment models by combining attention-based analysis and the integration of external knowledge.

Knowledge of tropical diseases and response capabilities of healthcare providers in Kaduna State, Nigeria

BACKGROUND

The public health impact of neglected tropical diseases (NTDs) is quite substantial. The objective of this study was to assess the knowledge and response capability of health professionals regarding NTDs in Kaduna State, Nigeria.

METHODS

A pre-tested questionnaire with a Cronbach's α coefficient of 0.716 was administered to 350 health professionals. The questionnaire assessed the knowledge, resource availability and capacity to handle NTD cases.

RESULTS

Only 38 (12.6%) respondents were familiar with the World Health Organization's definition of NTDs. Although self-reported knowledge was highest for physicians (37 [82.2%]), there was no statistically significant knowledge disparity between cadres of health professionals. Only 12 (46.2%) practitioners in private health facilities reported adequate knowledge. The tier of practice was significantly associated with management of NTDs (χ2 = 10.545; df 2; p = 0.005). Only 24 (47.1%) medical laboratory scientists and 18 (40.0%) physicians had adequate clinical resources for management of NTDs. Nearly three-quarters (211 (70.1%)] of respondents had never been trained in the management of NTDs. More than half (177 [58.8%]) of facilities lacked pharmaceuticals or standard operating procedures for management of NTDs.

CONCLUSIONS

Self-reported knowledge of NTDs was suboptimal. Physical and clinical resources for the diagnosis and treatment of NTDs were inadequate. Targeted training, increased funding and provision of adequate resources are needed in order to ameliorate the situation.

Multidisciplinary Development and Initial Validation of a Clinical Knowledge Base on Chronic Respiratory Diseases for mHealth Decision Support Systems

Most mobile health (mHealth) decision support systems currently available for chronic obstructive respiratory diseases (CORDs) are not supported by clinical evidence or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system is a crucial step that involves the collection and systematization of clinical knowledge from relevant scientific sources and its representation in a human-understandable and computer-interpretable way. This work describes the development and initial validation of a clinical knowledge base that can be integrated into mHealth decision support systems developed for patients with CORDs. A multidisciplinary team of health care professionals with clinical experience in respiratory diseases, together with data science and IT professionals, defined a new framework that can be used in other evidence-based systems. The knowledge base development began with a thorough review of the relevant scientific sources (eg, disease guidelines) to identify the recommendations to be implemented in the decision support system based on a consensus process. Recommendations were selected according to predefined inclusion criteria: (1) applicable to individuals with CORDs or to prevent CORDs, (2) directed toward patient self-management, (3) targeting adults, and (4) within the scope of the knowledge domains and subdomains defined. Then, the selected recommendations were prioritized according to (1) a harmonized level of evidence (reconciled from different sources); (2) the scope of the source document (international was preferred); (3) the entity that issued the source document; (4) the operability of the recommendation; and (5) health care professionals' perceptions of the relevance, potential impact, and reach of the recommendation. A total of 358 recommendations were selected. Next, the variables required to trigger those recommendations were defined (n=116) and operationalized into logical rules using Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component and pretested with an asthma use case. Initial validation of the knowledge base was conducted internally using data from a population-based observational study of individuals with or without asthma or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by 4 physicians. In addition, a strategy for knowledge base updates, including an easy-to-use rules editor, was defined. Using this process, based on consensus and iterative improvement, we developed and conducted preliminary validation of a clinical knowledge base for CORDs that translates disease guidelines into personalized patient recommendations. The knowledge base can be used as part of mHealth decision support systems. This process could be replicated in other clinical areas.

RatDEGdb: a knowledge base of differentially expressed genes in the rat as a model object in biomedical research

The animal models used in biomedical research cover virtually every human disease. RatDEGdb, a knowledge base of the differentially expressed genes (DEGs) of the rat as a model object in biomedical research is a collection of published data on gene expression in rat strains simulating arterial hypertension, age-related diseases, psychopathological conditions and other human afflictions. The current release contains information on 25,101 DEGs representing 14,320 unique rat genes that change transcription levels in 21 tissues of 10 genetic rat strains used as models of 11 human diseases based on 45 original scientific papers. RatDEGdb is novel in that, unlike any other biomedical database, it offers the manually curated annotations of DEGs in model rats with the use of independent clinical data on equal changes in the expression of homologous genes revealed in people with pathologies. The rat DEGs put in RatDEGdb were annotated with equal changes in the expression of their human homologs in affected people. In its current release, RatDEGdb contains 94,873 such annotations for 321 human genes in 836 diseases based on 959 original scientific papers found in the current PubMed. RatDEGdb may be interesting first of all to human geneticists, molecular biologists, clinical physicians, genetic advisors as well as experts in biopharmaceutics, bioinformatics and personalized genomics. RatDEGdb is publicly available at https://www.sysbio.ru/RatDEGdb.

Revised fission yeast gene and allele nomenclature guidelines for machine readability

Standardized nomenclature for genes, gene products, and isoforms is crucial to prevent ambiguity and enable clear communication of scientific data, facilitating efficient biocuration and data sharing. Standardized genotype nomenclature, which describes alleles present in a specific strain that differ from those in the wild-type reference strain, is equally essential to maximize research impact and ensure that results linking genotypes to phenotypes are Findable, Accessible, Interoperable, and Reusable (FAIR). In this publication, we extend the fission yeast clade gene nomenclature guidelines to support the curation efforts at PomBase (www.pombase.org), the Schizosaccharomyces pombe Model Organism Database. This update introduces nomenclature guidelines for noncoding RNA genes, following those set forth by the Human Genome Organisation Gene Nomenclature Committee. Additionally, we provide a significant update to the allele and genotype nomenclature guidelines originally published in 1987, to standardize the diverse range of genetic modifications enabled by the fission yeast genetic toolbox. These updated guidelines reflect a community consensus between numerous fission yeast researchers. Adoption of these rules will improve consistency in gene and genotype nomenclature, and facilitate machine-readability and automated entity recognition of fission yeast genes and alleles in publications or datasets. In conclusion, our updated guidelines provide a valuable resource for the fission yeast research community, promoting consistency, clarity, and FAIRness in genetic data sharing and interpretation.

A proteogenomics data-driven knowledge base of human cancer

By combining mass-spectrometry-based proteomics and phosphoproteomics with genomics, epi-genomics, and transcriptomics, proteogenomics provides comprehensive molecular characterization of cancer. Using this approach, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has characterized over 1,000 primary tumors spanning 10 cancer types, many with matched normal tissues. Here, we present LinkedOmicsKB, a proteogenomics data-driven knowledge base that makes consistently processed and systematically precomputed CPTAC pan-cancer proteogenomics data available to the public through ∼40,000 gene-, protein-, mutation-, and phenotype-centric web pages. Visualization techniques facilitate efficient exploration and reasoning of complex, interconnected data. Using three case studies, we illustrate the practical utility of LinkedOmicsKB in providing new insights into genes, phosphorylation sites, somatic mutations, and cancer phenotypes. With precomputed results of 19,701 coding genes, 125,969 phosphosites, and 256 genotypes and phenotypes, LinkedOmicsKB provides a comprehensive resource to accelerate proteogenomics data-driven discoveries to improve our understanding and treatment of human cancer. A record of this paper's transparent peer review process is included in the supplemental information.

A computer-aided diagnosis system for brain tumors based on artificial intelligence algorithms

The choice of treatment and prognosis evaluation depend on the accurate early diagnosis of brain tumors. Many brain tumors go undiagnosed or are overlooked by clinicians as a result of the challenges associated with manually evaluating magnetic resonance imaging (MRI) images in clinical practice. In this study, we built a computer-aided diagnosis (CAD) system for glioma detection, grading, segmentation, and knowledge discovery based on artificial intelligence algorithms. Neuroimages are specifically represented using a type of visual feature known as the histogram of gradients (HOG). Then, through a two-level classification framework, the HOG features are employed to distinguish between healthy controls and patients, or between different glioma grades. This CAD system also offers tumor visualization using a semi-automatic segmentation tool for better patient management and treatment monitoring. Finally, a knowledge base is created to offer additional advice for the diagnosis of brain tumors. Based on our proposed two-level classification framework, we train models for glioma detection and grading, achieving area under curve (AUC) of 0.921 and 0.806, respectively. Different from other systems, we integrate these diagnostic tools with a web-based interface, which provides the flexibility for system deployment.

CoVigator-A Knowledge Base for Navigating SARS-CoV-2 Genomic Variants

BACKGROUND

The outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) resulted in the global COVID-19 pandemic. The urgency for an effective SARS-CoV-2 vaccine has led to the development of the first series of vaccines at unprecedented speed. The discovery of SARS-CoV-2 spike-glycoprotein mutants, however, and consequentially the potential to escape vaccine-induced protection and increased infectivity, demonstrates the persisting importance of monitoring SARS-CoV-2 mutations to enable early detection and tracking of genomic variants of concern.

RESULTS

We developed the CoVigator tool with three components: (1) a knowledge base that collects new SARS-CoV-2 genomic data, processes it and stores its results; (2) a comprehensive variant calling pipeline; (3) an interactive dashboard highlighting the most relevant findings. The knowledge base routinely downloads and processes virus genome assemblies or raw sequencing data from the COVID-19 Data Portal (C19DP) and the European Nucleotide Archive (ENA), respectively. The results of variant calling are visualized through the dashboard in the form of tables and customizable graphs, making it a versatile tool for tracking SARS-CoV-2 variants. We put a special emphasis on the identification of intrahost mutations and make available to the community what is, to the best of our knowledge, the largest dataset on SARS-CoV-2 intrahost mutations. In the spirit of open data, all CoVigator results are available for download. The CoVigator dashboard is accessible via covigator.tron-mainz.de.

CONCLUSIONS

With increasing demand worldwide in genome surveillance for tracking the spread of SARS-CoV-2, CoVigator will be a valuable resource of an up-to-date list of mutations, which can be incorporated into global efforts.

Ex vivo proton spectroscopy (1 H-NMR) analysis of inborn errors of metabolism: Automatic and computer-assisted analyses

There are about 1500 genetic metabolic diseases. A small number of treatable diseases are diagnosed by newborn screening programs, which are continually being developed. However, most diseases can only be diagnosed based on clinical symptoms or metabolic findings. The main biological fluids used are urine, plasma and, in special situations, cerebrospinal fluid. In contrast to commonly used methods such as gas chromatography and high performance liquid chromatography mass spectrometry, ex vivo proton spectroscopy (¹ H-NMR) is not yet used in routine clinical practice, although it has been recommended for more than 30 years. Automatic analysis and improved NMR technology have also expanded the applications used for the diagnosis of inborn errors of metabolism. We provide a mini-overview of typical applications, especially in urine but also in plasma, used to diagnose common but also rare genetic metabolic diseases with ¹ H-NMR. The use of computer-assisted diagnostic suggestions can facilitate interpretation of the profiles. In a proof of principle, to date, 182 reports of 59 different diseases and 500 reports of healthy children are stored. The percentage of correct automatic diagnoses was 74%. Using the same ¹ H-NMR profile-targeted analysis, it is possible to apply an untargeted approach that distinguishes profile differences from healthy individuals. Thus, additional conditions such as lysosomal storage diseases or drug interferences are detectable. Furthermore, because ¹ H-NMR is highly reproducible and can detect a variety of different substance categories, the metabolomic approach is suitable for monitoring patient treatment and revealing additional factors such as nutrition and microbiome metabolism. Besides the progress in analytical techniques, a multiomics approach is most effective to combine metabolomics with, for example, whole exome sequencing, to also diagnose patients with nondetectable metabolic abnormalities in biological fluids. In this mini review we also provide our own data to demonstrate the role of NMR in a multiomics platform in the field of inborn errors of metabolism.

A Design for Safety (DFS) Framework for Automated Inspection Risks in Metro Stations by Integrating a Knowledge Base and Building Information Modeling

Safety issues have always been of great concern to the metro construction industry. Numerous studies have shown that safety issues are closely related to the design phase. Many safety problems can be solved or improved by developing the design. This study proposes a structured identification method for safety risks based on the metro design specifications, journal literature, and expert experience. A safety knowledge base (KB) for the design was established to realize safety knowledge sharing and reusing. The KB has been developed into Building Information Modeling (BIM) software as an inspection plug-in to achieve automated analysis and retrieval of safety risks. The designers are provided with a visualization of risk components to locate and improve the pre-control measures of the design. Subsequently, the process of design for safety (DFS) database creation was demonstrated with a metro station project, and the feasibility of applying the KB to safety checking in BIM was verified. In response to the inspection results, safety risks in the construction phases can be eliminated or avoided by standardizing and improving the design.

Knowledge-Based Dietary Intake Recommendations of Nutrients for Pediatric Patients with Maple Syrup Urine Disease

Maple syrup urine disease (MSUD) is a metabolic disorder characterized by a difficulty to digest and process proteins necessary for growth. To monitor and maintain the ideal growth of children with MSUD, caregivers need to carefully control the consumption of harmful branched-chain amino acids (BCAAs). The dietary limits of amino acids for MSUD patients are recommended and controlled by pediatricians and metabolic dietitians according to age, height, weight, and the prevailing percentage of amino acids in the body. This study introduces an intelligent dietary tool called MSUD Baby Buddy for caregivers of MSUD patients that tracks the amino acids intake out of baby formulas for babies 0-6 months old. This tool aims to provide accurate recommendations of the appropriate daily intake of protein and BCAAs based on the patients' data, plasma BCAAs, and formula preferences. We use a knowledge-based system, including knowledge acquisition and verification, as well as knowledge management tool validation, and the ripple-down rules are employed for building the system. MSUD Baby Buddy can support the maintenance of adequate amino acid levels and increase awareness about the control of BCAAs. The average usability of MSUD Baby Buddy is 84.25, indicating that the tool is intuitive and may help caregivers to easily determine the recommended doses of formula based on patients' biometric data and preferred formula. On the other hand, interviews with metabolic dietitians revealed some drawbacks, which were addressed to further improve the tool. MSUD Baby Buddy is expected to help caregivers of MSUD patients to independently track nutrient intake and reduce the number of visits to the pediatrician and metabolic dietitian.

Trusted Multi-Domain DDoS Detection Based on Federated Learning

Aiming at the problems of single detection target of existing distributed denial of service (DDoS) attacks, incomplete detection datasets and privacy caused by shared datasets, we propose a trusted multi-domain DDoS detection method based on federated learning. Firstly, we divide the types of DDoS attacks into different sub-attacks, design the federated learning dataset for DDoS detection in each domain, and use them to realize a more comprehensive detection method of DDoS attacks on the premise of protecting the data privacy of each domain. Secondly, in order to improve the robustness of federated learning and alleviate poisoning attack, we propose a reputation evaluation method based on blockchain, which estimates interaction reputation, data reputation and resource reputation of each participant comprehensively, so as to obtain the trusted federated learning participants and identify the malicious participants. In addition, we also propose a combination scheme of multi-domain detection and distributed knowledge base and design a feature graph of malicious behavior based on a knowledge graph to realize the memory of multi-domain feature knowledge. The experimental results show that the accuracy of most categories of the multi-domain DDoS detection method can reach more than 95% with the protection of datasets, and the reputation evaluation method proposed in this paper has a higher ability to identify malicious participants against the data poisoning attack when the threshold is set to 0.6.

Home Cancer Care Research: A Bibliometric and Visualization Analysis (1990-2021)

Home cancer care research (HCCR) has accelerated, as considerable attention has been placed on reducing cancer-related health costs and enhancing cancer patients' quality of life. Understanding the current status of HCCR can help guide future research and support informed decision-making about new home cancer care (HCC) programs. However, most current studies mainly detail the research status of certain components, while failing to explore the knowledge domain of this research field as a whole, thereby limiting the overall understanding of home cancer care. We carried out bibliometric and visualization analyses of Scopus-indexed papers related to home cancer care published between 1990-2021, and used VOSviewer scientometric software to investigate the status and provide a structural overview of the knowledge domain of HCCR (social, intellectual, and conceptual structures). Our findings demonstrate that over the last three decades, the research on home cancer care has been increasing, with a constantly expanding stream of new papers built on a solid knowledge base and applied to a wide range of research themes.

Knowledge-Based Design Algorithm for Support Reduction in Material Extrusion Additive Manufacturing

Although additive manufacturing (AM) enables designers to develop products with a high degree of design freedom, the manufacturing constraints of AM restrict design freedom. One of the key manufacturing constraints is the use of support structures for overhang features, which are indispensable in AM processes, but increase material consumption, manufacturing costs, and build time. Therefore, controlling support structure generation is a significant issue in fabricating functional products directly using AM. The goal of this paper is to propose a knowledge-based design algorithm for reducing support structures whilst considering printability and as-printed quality. The proposed method consists of three steps: (1) AM ontology development, for characterizing a target AM process, (2) Surrogate model construction, for quantifying the impact of the AM parameters on as-printed quality, (3) Design and process modification, for reducing support structures and optimizing the AM parameters. The significance of the proposed method is to not only optimize process parameters, but to also control local geometric features for a better surface roughness and build time reduction. To validate the proposed algorithm, case studies with curve-based (1D), surface-based (2D), and volume (3D) models were carried out to prove the reduction of support generation and build time while maintaining surface quality.

Project, toolkit, and database of neuroinformatics ecosystem: A summary of previous studies on "Frontiers in Neuroinformatics"

In the field of neuroscience, the core of the cohort study project consists of collection, analysis, and sharing of multi-modal data. Recent years have witnessed a host of efficient and high-quality toolkits published and employed to improve the quality of multi-modal data in the cohort study. In turn, gleaning answers to relevant questions from such a conglomeration of studies is a time-consuming task for cohort researchers. As part of our efforts to tackle this problem, we propose a hierarchical neuroscience knowledge base that consists of projects/organizations, multi-modal databases, and toolkits, so as to facilitate researchers' answer searching process. We first classified studies conducted for the topic "Frontiers in Neuroinformatics" according to the multi-modal data life cycle, and from these studies, information objects as projects/organizations, multi-modal databases, and toolkits have been extracted. Then, we map these information objects into our proposed knowledge base framework. A Python-based query tool has also been developed in tandem for quicker access to the knowledge base, (accessible at https://github.com/Romantic-Pumpkin/PDT_fninf). Finally, based on the constructed knowledge base, we discussed some key research issues and underlying trends in different stages of the multi-modal data life cycle.

An Intelligent System for Medical Decision Support in Differential Diagnosis and Treatment of COVID-19

The development of electronic services to help doctors in the diagnosis and treatment of COVID-19 is discussed. The existing systems for such purposes are analyzed, and requirements for them are formulated. The architecture of an intelligent medical decision support system and the basic principles of its development using an ontology-oriented approach are given. The unique capabilities of the system are shown, and its information and software components are described.

OncoPubMiner: a platform for mining oncology publications

Updated and expert-quality knowledge bases are fundamental to biomedical research. A knowledge base established with human participation and subject to multiple inspections is needed to support clinical decision making, especially in the growing field of precision oncology. The number of original publications in this field has risen dramatically with the advances in technology and the evolution of in-depth research. Consequently, the issue of how to gather and mine these articles accurately and efficiently now requires close consideration. In this study, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customisation, publication search with online reading and project-centred and team-based data collection to form a one-stop 'keyword in-knowledge out' oncology publication mining platform. The platform was constructed by integrating all open-access abstracts from PubMed and full-text articles from PubMed Central, and it is updated daily. OncoPubMiner makes obtaining precision oncology knowledge from scientific articles straightforward and will assist researchers in efficiently developing structured knowledge base systems and bring us closer to achieving precision oncology goals.

The Influence of Knowledge Base on the Dual-Innovation Performance of Firms

Dual innovation, which includes exploratory innovation and exploitative innovation, is crucial for firms to obtain a sustainable competitive advantage. The knowledge base of firms greatly influences or even determines the scope, direction, and path of their dual-innovation activities, which drive their innovation process and produce different innovation performances. This study uses data source patents obtained by 285 focal firms in the Chinese new-energy vehicle industry in the period 2015–2020. Five knowledge-base features are selected by analyzing the correlation and multicollinearity, and four different firm clusters are found by using the k-means clustering algorithm. Based on the classification and regression tree (CART) algorithm, we mine the potential decision rules governing the dual-innovation performance of firms. The results show that the exploratory innovation performance of firms in different clusters is mainly affected by two different knowledge-base features. Knowledge-base scale is a key factor affecting the exploitative innovation performance of firms. Firms in different clusters can improve their dual-innovation performance by rationally tuning the combination of knowledge-base features.

Learning to Reason on Tree Structures for Knowledge-Based Visual Question Answering

Collaborative reasoning for knowledge-based visual question answering is challenging but vital and efficient in understanding the features of the images and questions. While previous methods jointly fuse all kinds of features by attention mechanism or use handcrafted rules to generate a layout for performing compositional reasoning, which lacks the process of visual reasoning and introduces a large number of parameters for predicting the correct answer. For conducting visual reasoning on all kinds of image-question pairs, in this paper, we propose a novel reasoning model of a question-guided tree structure with a knowledge base (QGTSKB) for addressing these problems. In addition, our model consists of four neural module networks: the attention model that locates attended regions based on the image features and question embeddings by attention mechanism, the gated reasoning model that forgets and updates the fused features, the fusion reasoning model that mines high-level semantics of the attended visual features and knowledge base and knowledge-based fact model that makes up for the lack of visual and textual information with external knowledge. Therefore, our model performs visual analysis and reasoning based on tree structures, knowledge base and four neural module networks. Experimental results show that our model achieves superior performance over existing methods on the VQA v2.0 and CLVER dataset, and visual reasoning experiments prove the interpretability of the model.

Cov19VaxKB: A Web-based Integrative COVID-19 Vaccine Knowledge Base

The development of SARS-CoV-2 vaccines during the COVID-19 pandemic has prompted the emergence of COVID-19 vaccine data. Timely access to COVID-19 vaccine information is crucial to researchers and public. To support more comprehensive annotation, integration, and analysis of COVID-19 vaccine information, we have developed Cov19VaxKB, a knowledge-focused COVID-19 vaccine database (http://www.violinet.org/cov19vaxkb/). Cov19VaxKB features comprehensive lists of COVID-19 vaccines, vaccine formulations, clinical trials, publications, news articles, and vaccine adverse event case reports. A web-based query interface enables comparison of product information and host responses among various vaccines. The knowledge base also includes a vaccine design tool for predicting vaccine targets and a statistical analysis tool that identifies enriched adverse events for FDA-authorized COVID-19 vaccines based on VAERS case report data. To support data exchange, Cov19VaxKB is synchronized with Vaccine Ontology and the Vaccine Investigation and Online Information Network (VIOLIN) database. The data integration and analytical features of Cov19VaxKB can facilitate vaccine research and development while also serving as a useful reference for the public.

Development and Implementation of Pediatric Nursing-Clinical Decision Support System for Hyperthermia: A Pre- and Post-Test

Firstly, we form the Pediatric Nursing-Knowledge Base for Hyperthermia, which combines publicly clinical practice guidelines and nursing routines of hyperthermia management. Then, following the nursing process framework, the system is developed by clinical decision support technology. Finally, a pre- and post-test is adopted to examine the effectiveness, usability and feasibility before and after using the system. Its effectiveness is examined by nursing records quality including completeness of nursing assessment, timeliness of nursing diagnosis, individualization of nursing interventions, and timeliness of nursing evaluation. Its usability and feasibility are assessed using the Clinical Nursing Information System Effectiveness Evaluation Scale. There is a significant difference between the two groups in effectiveness, usability and feasibility. Although the system is developed specifically for our hospital workflow and processes, the Pediatric Nursing-Knowledge Base for Hyperthermia and workflow for hyperthermia management in this study can be used as a reference to other hospitals.

Author-sourced capture of pathway knowledge in computable form using Biofactoid

Making the knowledge contained in scientific papers machine-readable and formally computable would allow researchers to take full advantage of this information by enabling integration with other knowledge sources to support data analysis and interpretation. Here we describe Biofactoid, a web-based platform that allows scientists to specify networks of interactions between genes, their products, and chemical compounds, and then translates this information into a representation suitable for computational analysis, search and discovery. We also report the results of a pilot study to encourage the wide adoption of Biofactoid by the scientific community.

Structured ergonomic guidance in early design phases by analysing the user-product interaction

Gathering information for an early, proactive integration of ergonomic user requirements is challenging due to the unstructured character of available knowledge. Knowledge acquisition and processing is therefore costly and time-consuming. This contribution presents and evaluates InProCo, an approach for structured ergonomic guidance that aims to improve accessibility and clarity of ergonomic requirements in early design phases by providing interaction-based ergonomic properties. InProCo reduces the complexity of gathering knowledge and provide a novel way to describe interactions. Within a three-stage evaluation process, a survey assessed standard interactions for completeness, the output given by the graphical user interface of the approach (GUI) was evaluated for correctness and the knowledge base was validated by comparing the approaches output with properties identified by participants within a one-day workshop. The results showed that there are enough predefined standard interactions, the output of the GUI is valid and the knowledge base contains high quality data. Practitioner Summary: InProCo is an approach that provides structured interaction-based ergonomic properties to improve accessibility and clarity of ergonomic requirements in early design phases. This contribution presents and evaluates InProCo building the prerequisite for further use in an industrial context.

Spiking Neural Networks and Hippocampal Function: A Web-Accessible Survey of Simulations, Modeling Methods, and Underlying Theories

Computational modeling has contributed to hippocampal research in a wide variety of ways and through a large diversity of approaches, reflecting the many advanced cognitive roles of this brain region. The intensively studied neuron type circuitry of the hippocampus is a particularly conducive substrate for spiking neural models. Here we present an online knowledge base of spiking neural network simulations of hippocampal functions. First, we overview theories involving the hippocampal formation in subjects such as spatial representation, learning, and memory. Then we describe an original literature mining process to organize published reports in various key aspects, including: (i) subject area (e.g., navigation, pattern completion, epilepsy); (ii) level of modeling detail (Hodgkin-Huxley, integrate-and-fire, etc.); and (iii) theoretical framework (attractor dynamics, oscillatory interference, self-organizing maps, and others). Moreover, every peer-reviewed publication is also annotated to indicate the specific neuron types represented in the network simulation, establishing a direct link with the Hippocampome.org portal. The web interface of the knowledge base enables dynamic content browsing and advanced searches, and consistently presents evidence supporting every annotation. Moreover, users are given access to several types of statistical reports about the collection, a selection of which is summarized in this paper. This open access resource thus provides an interactive platform to survey spiking neural network models of hippocampal functions, compare available computational methods, and foster ideas for suitable new directions of research.

PharmGKB, an Integrated Resource of Pharmacogenomic Knowledge

The Pharmacogenomics Knowledgebase (PharmGKB) is an integrated online knowledge resource for the understanding of how genetic variation contributes to variation in drug response. Our focus includes not only pharmacogenomic information useful for clinical implementation (e.g., drug dosing guidelines and annotated drug labels), but also information to catalyze scientific research and drug discovery (e.g., variant-drug annotations and drug-centered pathways). As of April 2021, the annotated content of PharmGKB spans 715 drugs, 1761 genes, 227 diseases, 165 clinical guidelines, and 784 drug labels. We have manually curated data from more than 9000 published papers to generate the content of PharmGKB. Recently, we have also implemented an automated natural language processing (NLP) tool to broaden our coverage of the pharmacogenomic literature. This article contains a basic protocol describing how to navigate the PharmGKB website to retrieve information on how genes and genetic variations affect drug efficacy and toxicity. It also includes a protocol on how to use PharmGKB to facilitate interpretation of findings for a pharmacogenomic variant genotype or metabolizer phenotype. PharmGKB is freely available at http://www.pharmgkb.org. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Navigating the homepage of PharmGKB and searching by drug Basic Protocol 2: Using PharmGKB to facilitate interpretation of pharmacogenomic variant genotypes or metabolizer phenotypes.

Development and Implementation of a Pediatric Nursing-Clinical Decision Support System for Hyperthermia: A Pre- and Post-test

This article describes the development process and application of the Pediatric Nursing-Clinical Decision Support System for Hyperthermia. Firstly, we formed the Pediatric Nursing-Knowledge Base for Hyperthermia, which combines publicly available clinical practice guidelines and nursing routines of hyperthermia management. Then, following the nursing process framework, the system was developed using clinical decision support technology. Finally, a pre- and post-test were adopted to examine the effectiveness, usability, and feasibility before (1st to 31st of August 2018) and after (1st to 31st of December 2019) using the system. Its effectiveness was examined by analysis of nursing records' quality, including completeness of nursing assessment, timeliness of nursing diagnosis, individualization of nursing interventions, and timeliness of nursing evaluation. Its usability and feasibility were assessed using the Clinical Nursing Information System Effectiveness Evaluation Scale. There was a significant difference between the two groups in effectiveness, usability, and feasibility. Although the system was developed specifically for our hospital workflow and processes, the Pediatric Nursing-Knowledge Base for Hyperthermia and workflow for hyperthermia management in this study can be used as a reference to other hospitals.

Relationship Between Knowledge Base and Innovation-Driven Growth: Moderated by Organizational Character

Purpose: On the background of innovation-driven growth strategy of the Chinese government, this study aims to explore the impact of the knowledge base on innovation-driven growth of a firm, which is moderated by organizational character. Design/methodology/approach: Based on the data of 965 Chinese listed companies, some hypotheses were tested using the method of hierarchical regression analysis. Findings: Organizational growth relies on both technological and business model innovations and their interactive effect. Knowledge base, both breadth and depth, makes a positive impact on the innovation-driven growth of an enterprise. In the impacting mechanism, an explicit organizational character not only has direct positive effects on business model innovation, it also strengthens the effect of knowledge breadth on business model innovation. On the contrary, an implicit organizational character is not significantly related to innovation. Research limitations/implications: In order to achieve growth, enterprises are suggested to adopt such dual innovation strategy, led by technological innovation and supplemented with business model innovation, which is supported by the integrated management of intangible resources, deep and broad knowledge, and explicit organizational character. Originality/value: A new theoretical framework of organizational innovation-driven growth was proposed. The realization paths of innovation-driven growth were explored. The idea of collaborative governance between the knowledge base and organizational character was raised.

Successful deployment of drug-disease interaction clinical decision support across multiple Kaiser Permanente regions

OBJECTIVE

The study sought to develop a criteria-based scoring tool for assessing drug-disease knowledge base content and creation of a subset and to implement the subset across multiple Kaiser Permanente (KP) regions.

MATERIALS AND METHODS

In Phase I, the scoring tool was developed, used to create a drug-disease alert subset, and validated by surveying physicians and pharmacists from KP Northern California. In Phase II, KP enabled the alert subset in July 2015 in silent mode to collect alert firing rates and confirmed that alert burden was adequately reduced. The alert subset was subsequently rolled out to users in KP Northern California. Alert data was collected September 2015 to August 2016 to monitor relevancy and override rates.

RESULTS

Drug-disease alert scoring identified 1211 of 4111 contraindicated drug-disease pairs for inclusion in the subset. The survey results showed clinician agreement with subset examples 92.3%-98.5% of the time. Postsurvey adjustments to the subset resulted in KP implementation of 1189 drug-disease alerts. The subset resulted in a decrease in monthly alerts from 32 045 to 1168. Postimplementation monthly physician alert acceptance rates ranged from 20.2% to 29.8%.

DISCUSSION

Our study shows that drug-disease alert scoring resulted in an alert subset that generated acceptable interruptive alerts while decreasing overall potential alert burden. Following the initial testing and implementation in its Northern California region, KP successfully implemented the disease interaction subset in 4 regions with additional regions planned.

CONCLUSIONS

Our approach could prevent undue alert burden when new alert categories are implemented, circumventing the need for trial live activations of full alert category knowledge bases.

Diverse Taxonomies for Diverse Chemistries: Enhanced Representation of Natural Product Metabolism in UniProtKB

The UniProt Knowledgebase UniProtKB is a comprehensive, high-quality, and freely accessible resource of protein sequences and functional annotation that covers genomes and proteomes from tens of thousands of taxa, including a broad range of plants and microorganisms producing natural products of medical, nutritional, and agronomical interest. Here we describe work that enhances the utility of UniProtKB as a support for both the study of natural products and for their discovery. The foundation of this work is an improved representation of natural product metabolism in UniProtKB using Rhea, an expert-curated knowledgebase of biochemical reactions, that is built on the ChEBI (Chemical Entities of Biological Interest) ontology of small molecules. Knowledge of natural products and precursors is captured in ChEBI, enzyme-catalyzed reactions in Rhea, and enzymes in UniProtKB/Swiss-Prot, thereby linking chemical structure data directly to protein knowledge. We provide a practical demonstration of how users can search UniProtKB for protein knowledge relevant to natural products through interactive or programmatic queries using metabolite names and synonyms, chemical identifiers, chemical classes, and chemical structures and show how to federate UniProtKB with other data and knowledge resources and tools using semantic web technologies such as RDF and SPARQL. All UniProtKB data are freely available for download in a broad range of formats for users to further mine or exploit as an annotation source, to enrich other natural product datasets and databases.

[Knowledge discovery of Dao-di herbs based on traditional experience and literature data]

"Dao-di herbs" is the original comprehensive index for the quality evaluation of traditional Chinese medicine, which has the characteristics of high quality and good effect. After more than 30 years of development, a large number of data, including text data, image data and other types, have been generated in the modern research on Dao-di herbs. It has the characteristics of large quantity, variety, scattered content and different structure, and the data types of each feature are isolated or less interrelated. Based on the literature published in CNKI journal database since 1999-2019, the data collection, selection and collation of 34 kinds of Dao-di herbs characteristics, micro characteristics, ecological characteristics and genetic characteristics were carried out. Using the knowledge base construction technology and combining these characteristics of the characteristics data of the herbs, the above characteristics data were standardized and regarded as a part of the knowledge base of the herbs. Based on a large number of data, this paper analyzes the knowledge composition, research status, comparison between modern and traditional producing areas of traditional Chinese medicine, and takes Salvia miltiorrhiza as an example to compare the ecological characteristics of modern and traditional producing areas. The construction of the knowledge base will facilitate the mining and utilization of the scientific data of Dao-di herbs, and provide resource management and basic guarantee for the transformation and promotion of scientific and technological achievements of Dao-di herbs.

Boosting Knowledge Base Automatically via Few-Shot Relation Classification

Relation classification (RC) aims at extracting structural information, i.e., triplets of two entities with a relation, from free texts, which is pivotal for automatic knowledge base construction. In this paper, we investigate a fully automatic method to train a RC model which facilitates to boost the knowledge base. Traditional RC models cannot extract new relations unseen during training since they define RC as a multiclass classification problem. The recent development of few-shot learning (FSL) provides a feasible way to accommodate to fresh relation types with a handful of examples. However, it requires a moderately large amount of training data to learn a promising few-shot RC model, which consumes expensive human labor. This issue recalls a kind of weak supervision methods, dubbed distant supervision (DS), which can generate the training data automatically. To this end, we propose to investigate the task of few-shot relation classification under distant supervision. As DS naturally brings in mislabeled training instances, to alleviate the negative impact, we incorporate various multiple instance learning methods into the classic prototypical networks, which can achieve sentence-level noise reduction. In experiments, we evaluate our proposed model under the standard N-way K-shot setting of few-shot learning. The experiment results show that our proposal achieves better performance.

Developing an ontology for representing the domain knowledge specific to non-pharmacological treatment for agitation in dementia

INTRODUCTION

A large volume of clinical care data has been generated for managing agitation in dementia. However, the valuable information in these data has not been used effectively to generate insights for improving the quality of care. Application of artificial intelligence technologies offers us enormous opportunities to reuse these data. For health data science to achieve this, this study focuses on using ontology to coding clinical knowledge for non-pharmacological treatment of agitation in a machine-readable format.

METHODS

The resultant ontology-Dementia-Related Agitation Non-Pharmacological Treatment Ontology (DRANPTO)-was developed using a method adopted from the NeOn methodology.

RESULTS

DRANPTO consisted of 569 concepts and 48 object properties. It meets the standards for biomedical ontology.

DISCUSSION

DRANPTO is the first comprehensive semantic representation of non-pharmacological management for agitation in dementia in the long-term care setting. As a knowledge base, it will play a vital role to facilitate the development of intelligent systems for managing agitation in dementia.

Improving accessibility and distinction between negative results in biomedical relation extraction

Accessible negative results are relevant for researchers and clinicians not only to limit their search space but also to prevent the costly re-exploration of research hypotheses. However, most biomedical relation extraction datasets do not seek to distinguish between a false and a negative relation among two biomedical entities. Furthermore, datasets created using distant supervision techniques also have some false negative relations that constitute undocumented/unknown relations (missing from a knowledge base). We propose to improve the distinction between these concepts, by revising a subset of the relations marked as false on the phenotype-gene relations corpus and give the first steps to automatically distinguish between the false (F), negative (N), and unknown (U) results. Our work resulted in a sample of 127 manually annotated FNU relations and a weighted-F1 of 0.5609 for their automatic distinction. This work was developed during the 6th Biomedical Linked Annotation Hackathon (BLAH6).

Dr AFC: drug repositioning through anti-fibrosis characteristic

Fibrosis is a key component in the pathogenic mechanism of a variety of diseases. These diseases involving fibrosis may share common mechanisms and therapeutic targets, and therefore common intervention strategies and medicines may be applicable for these diseases. For this reason, deliberately introducing anti-fibrosis characteristics into predictive modeling may lead to more success in drug repositioning. In this study, anti-fibrosis knowledge base was first built by collecting data from multiple resources. Both structural and biological profiles were then derived from the knowledge base and used for constructing machine learning models including Structural Profile Prediction Model (SPPM) and Biological Profile Prediction Model (BPPM). Three external public data sets were employed for validation purpose and further exploration of potential repositioning drugs in wider chemical space. The resulting SPPM and BPPM models achieve area under the receiver operating characteristic curve (area under the curve) of 0.879 and 0.972 in the training set, and 0.814 and 0.874 in the testing set. Additionally, our results also demonstrate that substantial amount of multi-targeting natural products possess notable anti-fibrosis characteristics and might serve as encouraging candidates in fibrosis treatment and drug repositioning. To leverage our methodology and findings, we developed repositioning prediction platform, drug repositioning based on anti-fibrosis characteristic that is freely accessible via https://www.biosino.org/drafc.

STAT: A Web-based Semantic Text Annotation Tool to Assist Building Mental Health Knowledge Base

Mental health problems are serious among American adults and many of them are turning to the Internet for help. However, online mental health information is not well-organized and in low quality. We are building a mental health knowledge base (MHKB) with evidence-based information extracted from scientific literature manually, but lacking efficiency. We envision to leverage collective wisdoms through crowdsourcing to speed up the curation of MHKB. In order to integrate with crowdsourcing platforms, we designed and prototyped a web-based annotation tool, STAT (Semantic Text Annotation Tool), with real-time annotation recommendation and annotation quality analysis, to facilitate management of laypeople annotators recruited through crowdsourcing to complete the necessary annotation tasks.

A Novel Intelligent Scan Assistant System for Early Pregnancy Diagnosis by Ultrasound: Clinical Decision Support System Evaluation Study

Background

Early pregnancy ultrasound scans are usually performed by nonexpert examiners in obstetrics/gynecology (OB/GYN) emergency departments. Establishing the precise diagnosis of pregnancy location is key for appropriate management of early pregnancies, and experts are usually able to locate a pregnancy in the first scan. A decision-support system based on a semantic, expert-validated knowledge base may improve the diagnostic performance of nonexpert examiners for early pregnancy transvaginal ultrasound.

Objective

This study aims to evaluate a novel Intelligent Scan Assistant System for early pregnancy ultrasound to diagnose the pregnancy location and determine the image quality.

Methods

Two trainees performed virtual transvaginal ultrasound examinations of early pregnancy cases with and without the system. The ultrasound images and reports were blindly reviewed by two experts using scoring methods. A diagnosis of pregnancy location and ultrasound image quality were compared between scans performed with and without the system.

Results

Each trainee performed a virtual vaginal examination for all 32 cases with and without use of the system. The analysis of the 128 resulting scans showed higher quality of the images (quality score: +23%; P<.001), less images per scan (4.6 vs 6.3 [without the CDSS]; P<.001), and higher confidence in reporting conclusions (trust score: +20%; P<.001) with use of the system. Further, use of the system cost an additional 8 minutes per scan. We observed a correct diagnosis of pregnancy location in 39 (61%) and 52 (81%) of 64 scans in the nonassisted mode and assisted mode, respectively. Additionally, an exact diagnosis (with precise ectopic location) was made in 30 (47%) and 49 (73%) of the 64 scans without and with use of the system, respectively. These differences in diagnostic performance (+20% for correct location diagnosis and +30% for exact diagnosis) were both statistically significant (P=.002 and P<.001, respectively).

Conclusions

The Intelligent Scan Assistant System is based on an expert-validated knowledge base and demonstrates significant improvement in early pregnancy scanning, both in diagnostic performance (pregnancy location and precise diagnosis) and scan quality (selection of images, confidence, and image quality).

Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners for decision-making about diagnosing some diseases. Within the CDSSs, we can find Fuzzy inference systems. For the reasons above, the objective of this study was to design, to implement, and to validate a methodology for developing data-driven Mamdani-type fuzzy clinical decision support systems using clusters and pivot tables. For validating the proposed methodology, we applied our algorithms on five public datasets including Wisconsin, Coimbra breast cancer, wart treatment (Immunotherapy and cryotherapy), and caesarian section, and compared them with other related works (Literature). The results show that the Kappa Statistics and accuracies were close to 1.0% and 100%, respectively for each output variable, which shows better accuracy than some literature results. The proposed framework could be considered as a deep learning technique because it is composed of various processing layers to learn representations of data with multiple levels of abstraction.

Prediction and Decision-Making in Intelligent Environments Supported by Knowledge Graphs, A Systematic Review

Ambient Intelligence is currently a lively application domain of Artificial Intelligence and has become the central subject of multiple initiatives worldwide. Several approaches inside this domain make use of knowledge bases or knowledge graphs, both previously existing and ad hoc. This form of representation allows heterogeneous data gathered from diverse sources to be contextualized and combined to create relevant information for intelligent systems, usually following higher level constraints defined by an ontology. In this work, we conduct a systematic review of the existing usages of knowledge bases in intelligent environments, as well as an in-depth study of the predictive and decision-making models employed. Finally, we present a use case for smart homes and illustrate the use and advantages of Knowledge Graph Embeddings in this context.

Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose-Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose-Response Analysis

Quantitative risk assessments for physical, chemical, biological, occupational, or environmental agents rely on scientific studies to support their conclusions. These studies often include relatively few observations, and, as a result, models used to characterize the risk may include large amounts of uncertainty. The motivation, development, and assessment of new methods for risk assessment is facilitated by the availability of a set of experimental studies that span a range of dose-response patterns that are observed in practice. We describe construction of such a historical database focusing on quantal data in chemical risk assessment, and we employ this database to develop priors in Bayesian analyses. The database is assembled from a variety of existing toxicological data sources and contains 733 separate quantal dose-response data sets. As an illustration of the database's use, prior distributions for individual model parameters in Bayesian dose-response analysis are constructed. Results indicate that including prior information based on curated historical data in quantitative risk assessments may help stabilize eventual point estimates, producing dose-response functions that are more stable and precisely estimated. These in turn produce potency estimates that share the same benefit. We are confident that quantitative risk analysts will find many other applications and issues to explore using this database.

Identifying Principles for the Construction of an Ontology-Based Knowledge Base: A Case Study Approach

Background

Ontologies are key enabling technologies for the Semantic Web. The Web Ontology Language (OWL) is a semantic markup language for publishing and sharing ontologies.

Objective

The supply of customizable, computable, and formally represented molecular genetics information and health information, via electronic health record (EHR) interfaces, can play a critical role in achieving precision medicine. In this study, we used cystic fibrosis as an example to build an Ontology-based Knowledge Base prototype on Cystic Fibrobis (OntoKBCF) to supply such information via an EHR prototype. In addition, we elaborate on the construction and representation principles, approaches, applications, and representation challenges that we faced in the construction of OntoKBCF. The principles and approaches can be referenced and applied in constructing other ontology-based domain knowledge bases.

Methods

First, we defined the scope of OntoKBCF according to possible clinical information needs about cystic fibrosis on both a molecular level and a clinical phenotype level. We then selected the knowledge sources to be represented in OntoKBCF. We utilized top-to-bottom content analysis and bottom-up construction to build OntoKBCF. Protégé-OWL was used to construct OntoKBCF. The construction principles included (1) to use existing basic terms as much as possible; (2) to use intersection and combination in representations; (3) to represent as many different types of facts as possible; and (4) to provide 2-5 examples for each type. HermiT 1.3.8.413 within Protégé-5.1.0 was used to check the consistency of OntoKBCF.

Results

OntoKBCF was constructed successfully, with the inclusion of 408 classes, 35 properties, and 113 equivalent classes. OntoKBCF includes both atomic concepts (such as amino acid) and complex concepts (such as “adolescent female cystic fibrosis patient”) and their descriptions. We demonstrated that OntoKBCF could make customizable molecular and health information available automatically and usable via an EHR prototype. The main challenges include the provision of a more comprehensive account of different patient groups as well as the representation of uncertain knowledge, ambiguous concepts, and negative statements and more complicated and detailed molecular mechanisms or pathway information about cystic fibrosis.

Conclusions

Although cystic fibrosis is just one example, based on the current structure of OntoKBCF, it should be relatively straightforward to extend the prototype to cover different topics. Moreover, the principles underpinning its development could be reused for building alternative human monogenetic diseases knowledge bases.

Enhancing diversity to reduce health information disparities and build an evidence base for genomic medicine

Advances in genomic medicine are arising from efforts to build a national learning healthcare system (LHS) and large-scale precision medicine studies. However, the underlying evidence base lacks sufficient data from populations historically underrepresented in biomedical research. Although the literature on health and healthcare disparities is extensive, disparities in the availability and quality of health information about diverse and underrepresented populations are less well characterized. This Perspective describes scientific and ethical benefits to incorporating health information from diverse and underrepresented populations in the LHS, resulting in a more robust and generalizable LHS. Near-term recommendations for incorporating diversity into the evidence base for genomic medicine are proposed, even as the groundwork for national and international efforts is underway.

Assembling the Community-Scale Discoverable Human Proteome

The increasing throughput and sharing of proteomics mass spectrometry data have now yielded over one-third of a million public mass spectrometry runs. However, these discoveries are not continuously aggregated in an open and error-controlled manner, which limits their utility. To facilitate the reusability of these data, we built the MassIVE Knowledge Base (MassIVE-KB), a community-wide, continuously updating knowledge base that aggregates proteomics mass spectrometry discoveries into an open reusable format with full provenance information for community scrutiny. Reusing >31 TB of public human data stored in a mass spectrometry interactive virtual environment (MassIVE), the MassIVE-KB contains >2.1 million precursors from 19,610 proteins (48% larger than before; 97% of the total) and doubles proteome coverage to 6 million amino acids (54% of the proteome) with strict library-scale false discovery controls, thereby providing evidence for 430 proteins for which sufficient protein-level evidence was previously missing. Furthermore, MassIVE-KB can inform experimental design, helps identify and quantify new data, and provides tools for community construction of specialized spectral libraries.

Wang et al. introduce MassIVE-KB, a program designed to distill the entire community’s mass spectrometry data into reusable spectral library resources. As a result, the statistically-significant discovery of a peptide or protein in a single researcher’s data will thus be made available to the whole community to support its identification (in shotgun experiments) or quantitative detection (in targeted experiments) in all future analyses.

Mind the Gap: From Tool to Knowledge Base

With the ethical, legal, and societal issues (ELSI) Knowledge Base, we introduce a key element of the Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC) Common Service ELSI, which provides ethical, legal, and societal support for researchers and biobankers involved in transnational research. In contrast to the customized support provided by the ELSI Helpdesk, the ELSI Knowledge Base will be available to the user on a self-serve basis. The information that is made available through a knowledge base comes from multiple sources, usually from several expert contributors who are well versed in the subject matter. The knowledge base provides users with a first orientation on the subject matter, as well as allowing them to explore more detailed information if desired in a self-service manner. It is crucial that the information and knowledge provided are shared in a manner that is user friendly. Long lists of links, legalistic language, and multiple links have to be avoided wherever possible. The long-term sustainability and accuracy of a knowledge base need to be ensured by placing its expert curation and technical maintenance under the responsibility of an organization rather than a research consortium. In its core, it builds on a scenario-based approach using a nonlegalistic language. In addition, the knowledge base connects to frequently asked questions, promotes contract and informed consent templates, how-to-guides, best-practice models, and scripts. The ELSI Knowledge Base is a key element of the BBMRI-ERIC Common Service ELSI, which currently serves biobanks but will be enlarged to serve the biological and medical sciences community. In contrast to the ELSI Helpdesk, which provides customized support, the ELSI Knowledge Base is available to the user on a self-serve basis. The conceptualization of the ELSI Knowledge Base builds on assessments of several ethical, legal, and societal guidance tools that favor a single sustainable knowledge base for closing the knowledge gap by providing practical hands-on guidance for researchers. Ultimately, the ELSI Knowledge Base aims at promoting practical know-how and skills for conducting responsible research.

An Ontology-Based Knowledge Methodology in the Medical Domain in the Latin America: the Study Case of Republic of Panama

Introduction:

Nowadays in Panama, there is a lot of patient information stored in textual form which cannot be manipulated to manage adequate knowledge. There are multiple resources created to represent knowledge, including specialized glossaries, ontologies, among others. The ontologies are an important part within the scope of the recovery and organization of the information and the semantic web. Also in recent works they are used in applications of natural language processing (NLP), as a knowledge base.

Aim:

This research was conducted with the aim of creating a methodology that allows from a text written in NL, extract the necessary elements using NLP tools and with them create a knowledge base represented by one domain ontology and extract knowledge to help medical specialists.

Material and Methods:

In this study we carried out a methodology that allows the extraction of knowledge of patient clinical records, general medicine and palliative care, in order to show relevant knowledge elements to specialists. The methodology was validated with a data corpus of approximately 200 patient records.

Conclusion:

We have created a knowledge representation methodology, combining NLP techniques and tools and the automatic instantiation of an ontology, which can serve as a software agent for other applications or used to visualize the patient’s clinical information. The study was validated using the traditional metrics of information retrieval systems precision, recall, F-measure obtaining excellent results, and can be used as a software agent or methodology for the development of information extraction software systems in the medical domain.

Situation Model for Situation-Aware Assistance of Dementia Patients in Outdoor Mobility

Background:

Dementia impairs spatial orientation and route planning, thus often affecting the patient’s ability to move outdoors and maintain social activities. Situation-aware deliberative assistive technology devices (ATD) can substitute impaired cognitive function in order to maintain one’s level of social activity. To build such a system, one needs domain knowledge about the patient’s situation and needs. We call this collection of knowledge situation model.

Objective:

To construct a situation model for the outdoor mobility of people with dementia (PwD). The model serves two purposes: 1) as a knowledge base from which to build an ATD describing the mobility of PwD; and 2) as a codebook for the annotation of the recorded behavior.

Methods:

We perform systematic knowledge elicitation to obtain the relevant knowledge. The OBO Edit tool is used for implementing and validating the situation model. The model is evaluated by using it as a codebook for annotating the behavior of PwD during a mobility study and interrater agreement is computed. In addition, clinical experts perform manual evaluation and curation of the model.

Results:

The situation model consists of 101 concepts with 11 relation types between them. The results from the annotation showed substantial overlapping between two annotators (Cohen’s kappa of 0.61).

Conclusion:

The situation model is a first attempt to systematically collect and organize information related to the outdoor mobility of PwD for the purposes of situation-aware assistance. The model is the base for building an ATD able to provide situation-aware assistance and to potentially improve the quality of life of PwD.