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Sachdeva S, Bhatia S, Al Harrasi A, Shah YA, Anwer K, Philip AK, Shah SFA, Khan A, Ahsan Halim S. Unraveling the role of cloud computing in health care system and biomedical sciences. Heliyon 2024; 10:e29044. [PMID: 38601602 PMCID: PMC11004887 DOI: 10.1016/j.heliyon.2024.e29044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 03/24/2024] [Accepted: 03/28/2024] [Indexed: 04/12/2024] Open
Abstract
Cloud computing has emerged as a transformative force in healthcare and biomedical sciences, offering scalable, on-demand resources for managing vast amounts of data. This review explores the integration of cloud computing within these fields, highlighting its pivotal role in enhancing data management, security, and accessibility. We examine the application of cloud computing in various healthcare domains, including electronic medical records, telemedicine, and personalized patient care, as well as its impact on bioinformatics research, particularly in genomics, proteomics, and metabolomics. The review also addresses the challenges and ethical considerations associated with cloud-based healthcare solutions, such as data privacy and cybersecurity. By providing a comprehensive overview, we aim to assist readers in understanding the significance of cloud computing in modern medical applications and its potential to revolutionize both patient care and biomedical research.
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Affiliation(s)
| | - Saurabh Bhatia
- Natural & Medical Sciences Research Center, University of Nizwa, P.O. Box 33, 616 Birkat Al Mauz, Nizwa, Oman
- School of Health Science, University of Petroleum and Energy Studies, Prem Nagar, Dehradun, Uttarakhand, 248007, India
| | - Ahmed Al Harrasi
- Natural & Medical Sciences Research Center, University of Nizwa, P.O. Box 33, 616 Birkat Al Mauz, Nizwa, Oman
| | - Yasir Abbas Shah
- Natural & Medical Sciences Research Center, University of Nizwa, P.O. Box 33, 616 Birkat Al Mauz, Nizwa, Oman
| | - Khalid Anwer
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
| | - Anil K. Philip
- School of Pharmacy, University of Nizwa, Birkat Al Mouz, Nizwa, 616, Oman
| | - Syed Faisal Abbas Shah
- Faculty of Computer Science & Information Technology, Virtual University of Pakistan, Lahore, 54000, Pakistan
| | - Ajmal Khan
- Natural & Medical Sciences Research Center, University of Nizwa, P.O. Box 33, 616 Birkat Al Mauz, Nizwa, Oman
| | - Sobia Ahsan Halim
- Natural & Medical Sciences Research Center, University of Nizwa, P.O. Box 33, 616 Birkat Al Mauz, Nizwa, Oman
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Dall'Alba G, Casa PL, Abreu FPD, Notari DL, de Avila E Silva S. A Survey of Biological Data in a Big Data Perspective. BIG DATA 2022; 10:279-297. [PMID: 35394342 DOI: 10.1089/big.2020.0383] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The amount of available data is continuously growing. This phenomenon promotes a new concept, named big data. The highlight technologies related to big data are cloud computing (infrastructure) and Not Only SQL (NoSQL; data storage). In addition, for data analysis, machine learning algorithms such as decision trees, support vector machines, artificial neural networks, and clustering techniques present promising results. In a biological context, big data has many applications due to the large number of biological databases available. Some limitations of biological big data are related to the inherent features of these data, such as high degrees of complexity and heterogeneity, since biological systems provide information from an atomic level to interactions between organisms or their environment. Such characteristics make most bioinformatic-based applications difficult to build, configure, and maintain. Although the rise of big data is relatively recent, it has contributed to a better understanding of the underlying mechanisms of life. The main goal of this article is to provide a concise and reliable survey of the application of big data-related technologies in biology. As such, some fundamental concepts of information technology, including storage resources, analysis, and data sharing, are described along with their relation to biological data.
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Affiliation(s)
- Gabriel Dall'Alba
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
- Genome Science and Technology Program, Faculty of Science, The University of British Columbia, Vancouver, Canada
| | - Pedro Lenz Casa
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
| | - Fernanda Pessi de Abreu
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
| | - Daniel Luis Notari
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
| | - Scheila de Avila E Silva
- Computational Biology and Bioinformatics Laboratory, Biotechnology Institute, Department of Life Sciences, University of Caxias do Sul, Caxias do Sul, Brazil
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Banegas-Luna AJ, Peña-García J, Iftene A, Guadagni F, Ferroni P, Scarpato N, Zanzotto FM, Bueno-Crespo A, Pérez-Sánchez H. Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey. Int J Mol Sci 2021; 22:4394. [PMID: 33922356 PMCID: PMC8122817 DOI: 10.3390/ijms22094394] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/16/2021] [Accepted: 04/20/2021] [Indexed: 12/18/2022] Open
Abstract
Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine-specifically, to cancer research-and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors' predictive capacity and achieve individualised therapies in the near future.
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Affiliation(s)
- Antonio Jesús Banegas-Luna
- Structural Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain; (J.P.-G.); (A.B.-C.)
| | - Jorge Peña-García
- Structural Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain; (J.P.-G.); (A.B.-C.)
| | - Adrian Iftene
- Faculty of Computer Science, Universitatea Alexandru Ioan Cuza (UAIC), 700505 Jashi, Romania;
| | - Fiorella Guadagni
- Interinstitutional Multidisciplinary Biobank (BioBIM), IRCCS San Raffaele Roma, 00166 Rome, Italy; (F.G.); (P.F.)
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy;
| | - Patrizia Ferroni
- Interinstitutional Multidisciplinary Biobank (BioBIM), IRCCS San Raffaele Roma, 00166 Rome, Italy; (F.G.); (P.F.)
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy;
| | - Noemi Scarpato
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy;
| | - Fabio Massimo Zanzotto
- Dipartimento di Ingegneria dell’Impresa “Mario Lucertini”, University of Rome Tor Vergata, 00133 Rome, Italy;
| | - Andrés Bueno-Crespo
- Structural Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain; (J.P.-G.); (A.B.-C.)
| | - Horacio Pérez-Sánchez
- Structural Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107 Murcia, Spain; (J.P.-G.); (A.B.-C.)
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Schaduangrat N, Lampa S, Simeon S, Gleeson MP, Spjuth O, Nantasenamat C. Towards reproducible computational drug discovery. J Cheminform 2020; 12:9. [PMID: 33430992 PMCID: PMC6988305 DOI: 10.1186/s13321-020-0408-x] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 01/02/2020] [Indexed: 12/11/2022] Open
Abstract
The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.
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Affiliation(s)
- Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand
| | - Samuel Lampa
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | - Saw Simeon
- Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, 10900, Bangkok, Thailand
| | - Matthew Paul Gleeson
- Department of Biomedical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, 10520, Bangkok, Thailand.
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden.
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand.
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Lambrinidis G, Tsantili-Kakoulidou A. Challenges with multi-objective QSAR in drug discovery. Expert Opin Drug Discov 2018; 13:851-859. [DOI: 10.1080/17460441.2018.1496079] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- George Lambrinidis
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Zografou, Athens, Greece
| | - Anna Tsantili-Kakoulidou
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Zografou, Athens, Greece
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Sobeslav V, Maresova P, Krejcar O, Franca TC, Kuca K. Use of cloud computing in biomedicine. J Biomol Struct Dyn 2016; 34:2688-2697. [DOI: 10.1080/07391102.2015.1127182] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Gupta SK. Paperless clinical trials: Myth or reality? Indian J Pharmacol 2015; 47:349-53. [PMID: 26288464 PMCID: PMC4527052 DOI: 10.4103/0253-7613.161247] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2014] [Revised: 02/26/2015] [Accepted: 06/07/2015] [Indexed: 11/06/2022] Open
Abstract
There is an urgent need to expedite the time-to-market for new drugs and to make the approval process simpler. But clinical trials are a complex process and the increased complexity leads to decreased efficiency. Hence, pharmaceutical organizations want to move toward a more technology-driven clinical trial process for recording, analyzing, reporting, archiving, etc., In recent times, the progress has certainly been made in developing paperless systems that improve data capture and management. The adaptation of paperless processes may require major changes to existing procedures. But this is in the best interests of these organizations to remain competitive because a paperless clinical trial would lead to a consistent and streamlined framework. Moreover, all major regulatory authorities also advocate adoption of paperless trial. But challenges still remain toward implementation of paperless clinical trial process.
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Affiliation(s)
- Sandeep K Gupta
- Department of Pharmacology, Dhanalakshmi Srinivasan Medical College and Hospital, Siruvachur, Perambalur, Tamil Nadu, India
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Holzinger A, Dehmer M, Jurisica I. Knowledge Discovery and interactive Data Mining in Bioinformatics--State-of-the-Art, future challenges and research directions. BMC Bioinformatics 2014; 15 Suppl 6:I1. [PMID: 25078282 PMCID: PMC4140208 DOI: 10.1186/1471-2105-15-s6-i1] [Citation(s) in RCA: 134] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Affiliation(s)
- Andreas Holzinger
- Research Unit Human-Computer Interaction, Austrian IBM Watson Think Group, Institute for Medical Informatics, Statistics & Documentation, Medical University Graz, Austria
- Institute of Information Systems and Computer Media, Graz University of Technology, Austria
| | - Matthias Dehmer
- Institute for Bioinformatics and Translational Research, UMIT Tyrol, Austria
| | - Igor Jurisica
- Departments of Medical Biophysics and Computer Science, University of Toronto, Ontario, Canada
- Princess Margaret Cancer Centre and Techna Institute for the Advancement of Technology for Health, University Health Network, IBM Life Sciences Discovery Centre, Ontario, Canada
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Cloud infrastructures for in silico drug discovery: economic and practical aspects. BIOMED RESEARCH INTERNATIONAL 2013; 2013:138012. [PMID: 24106693 PMCID: PMC3782806 DOI: 10.1155/2013/138012] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2013] [Revised: 06/26/2013] [Accepted: 06/27/2013] [Indexed: 11/17/2022]
Abstract
Cloud computing opens new perspectives for small-medium biotechnology laboratories that need to perform bioinformatics analysis in a flexible and effective way. This seems particularly true for hybrid clouds that couple the scalability offered by general-purpose public clouds with the greater control and ad hoc customizations supplied by the private ones. A hybrid cloud broker, acting as an intermediary between users and public providers, can support customers in the selection of the most suitable offers, optionally adding the provisioning of dedicated services with higher levels of quality. This paper analyses some economic and practical aspects of exploiting cloud computing in a real research scenario for the in silico drug discovery in terms of requirements, costs, and computational load based on the number of expected users. In particular, our work is aimed at supporting both the researchers and the cloud broker delivering an IaaS cloud infrastructure for biotechnology laboratories exposing different levels of nonfunctional requirements.
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Zhang X, Wong SE, Lightstone FC. Message passing interface and multithreading hybrid for parallel molecular docking of large databases on petascale high performance computing machines. J Comput Chem 2013; 34:915-27. [PMID: 23345155 DOI: 10.1002/jcc.23214] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2012] [Revised: 10/03/2012] [Accepted: 11/28/2012] [Indexed: 02/06/2023]
Abstract
A mixed parallel scheme that combines message passing interface (MPI) and multithreading was implemented in the AutoDock Vina molecular docking program. The resulting program, named VinaLC, was tested on the petascale high performance computing (HPC) machines at Lawrence Livermore National Laboratory. To exploit the typical cluster-type supercomputers, thousands of docking calculations were dispatched by the master process to run simultaneously on thousands of slave processes, where each docking calculation takes one slave process on one node, and within the node each docking calculation runs via multithreading on multiple CPU cores and shared memory. Input and output of the program and the data handling within the program were carefully designed to deal with large databases and ultimately achieve HPC on a large number of CPU cores. Parallel performance analysis of the VinaLC program shows that the code scales up to more than 15K CPUs with a very low overhead cost of 3.94%. One million flexible compound docking calculations took only 1.4 h to finish on about 15K CPUs. The docking accuracy of VinaLC has been validated against the DUD data set by the re-docking of X-ray ligands and an enrichment study, 64.4% of the top scoring poses have RMSD values under 2.0 Å. The program has been demonstrated to have good enrichment performance on 70% of the targets in the DUD data set. An analysis of the enrichment factors calculated at various percentages of the screening database indicates VinaLC has very good early recovery of actives.
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Affiliation(s)
- Xiaohua Zhang
- Biosciences & Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Lab, Livermore, CA 94550, USA
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