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Guo H, Scriney M, Liu K. An Ostensive Information Architecture to Enhance Semantic Interoperability for Healthcare Information Systems. INFORMATION SYSTEMS FRONTIERS : A JOURNAL OF RESEARCH AND INNOVATION 2023:1-24. [PMID: 37361885 PMCID: PMC9974391 DOI: 10.1007/s10796-023-10379-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/13/2023] [Indexed: 06/28/2023]
Abstract
Semantic interoperability establishes intercommunications and enables data sharing across disparate systems. In this study, we propose an ostensive information architecture for healthcare information systems to decrease ambiguity caused by using signs in different contexts for different purposes. The ostensive information architecture adopts a consensus-based approach initiated from the perspective of information systems re-design and can be applied to other domains where information exchange is required between heterogeneous systems. Driven by the issues in FHIR (Fast Health Interoperability Resources) implementation, an ostensive approach that supplements the current lexical approach in semantic exchange is proposed. A Semantic Engine with an FHIR knowledge graph as the core is constructed using Neo4j to provide semantic interpretation and examples. The MIMIC III (Medical Information Mart for Intensive Care) datasets and diabetes datasets have been employed to demonstrate the effectiveness of the proposed information architecture. We further discuss the benefits of the separation of semantic interpretation and data storage from the perspective of information system design, and the semantic reasoning towards patient-centric care underpinned by the Semantic Engine.
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Affiliation(s)
- Hua Guo
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
- Insight Centre for Data Analytics, School of Computing, Dublin City University, Dublin, Ireland
- Informatics Research Centre, University of Reading, Reading, UK
| | - Michael Scriney
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
- Insight Centre for Data Analytics, School of Computing, Dublin City University, Dublin, Ireland
- Informatics Research Centre, University of Reading, Reading, UK
| | - Kecheng Liu
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
- Insight Centre for Data Analytics, School of Computing, Dublin City University, Dublin, Ireland
- Informatics Research Centre, University of Reading, Reading, UK
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2
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Del Casale A, Sarli G, Bargagna P, Polidori L, Alcibiade A, Zoppi T, Borro M, Gentile G, Zocchi C, Ferracuti S, Preissner R, Simmaco M, Pompili M. Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry. Curr Neuropharmacol 2023; 21:2395-2408. [PMID: 37559539 PMCID: PMC10616924 DOI: 10.2174/1570159x21666230808170123] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/01/2022] [Accepted: 12/06/2022] [Indexed: 08/11/2023] Open
Abstract
Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they will gain a unique role in the next future in improving personalized treatments in psychiatry.
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Affiliation(s)
- Antonio Del Casale
- Department of Dynamic and Clinical Psychology and Health Studies, Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Giuseppe Sarli
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Paride Bargagna
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Lorenzo Polidori
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Alessandro Alcibiade
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Teodolinda Zoppi
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Marina Borro
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Laboratory and Advanced Molecular Diagnostics, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Giovanna Gentile
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Laboratory and Advanced Molecular Diagnostics, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Clarissa Zocchi
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Stefano Ferracuti
- Department of Human Neuroscience, Faculty of Medicine and Dentistry, Sapienza University, Unit of Risk Management, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Robert Preissner
- Institute of Physiology and Science-IT, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Philippstrasse 12, 10115, Berlin, Germany
| | - Maurizio Simmaco
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Laboratory and Advanced Molecular Diagnostics, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Maurizio Pompili
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
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Rudrapal M, Issahaku AR, Agoni C, Bendale AR, Nagar A, Soliman MES, Lokwani D. In silico screening of phytopolyphenolics for the identification of bioactive compounds as novel protease inhibitors effective against SARS-CoV-2. J Biomol Struct Dyn 2022; 40:10437-10453. [PMID: 34182889 DOI: 10.1080/07391102.2021.1944909] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Due to the unavailability specific drugs or vaccines (FDA approved) that can cure COVID-19, the development of potent antiviral drug candidates/therapeutic molecules against COVID-19 is urgently required. This study was aimed at in silico screening and study of polyphenolic phytochemical compounds in a rational way by virtual screening, molecular docking and molecular dynamics studies against SARS-CoV-2 main protease (Mpro) and papain-like protease (PLpro) enzymes. The objective of the study was to identify plant-derived polyphenolic compounds and/or flavonoid molecules as possible antiviral agents with protease inhibitory potential against SARS-CoV-2. In this study, we report plant-derived polyphenolic compounds (including flavonoids) as novel protease inhibitors against SARS-CoV-2. From virtual docking and molecular docking study, 31 polyphenolic compounds were identified as active antiviral molecules possessing well-defined binding affinity with acceptable ADMET, toxicity and lead-like or drug-like properties. Six polyphenolic compounds, namely, enterodiol, taxifolin, eriodictyol, leucopelargonidin, morin and myricetin were found to exhibit remarkable binding affinities against the proteases with taxifolin and morin exhibiting the highest binding affinity toward Mpro and PLpro respectively. Molecular dynamics simulation studies of these compounds in complex with the proteases showed that the binding of the compounds is characterized by structural perturbations of the proteases suggesting their antiviral activities. These compounds can therefore be investigated further by in vivo and in vitro techniques to assess their potential efficacy against SARS-CoV-2 and thus serve as the starting point for the development of potent antiviral agents against the deadly COVID-19.
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Affiliation(s)
- Mithun Rudrapal
- Rasiklal M. Dhariwal Institute of Pharmaceutical Education and Research, Pune, Maharashtra, India
| | - Abdul Rashid Issahaku
- Molecular Bio-Computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Clement Agoni
- Molecular Bio-Computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Atul R Bendale
- Sandip Institute of Pharmaceutical Sciences, Nashik, Maharashtra, India
| | - Akhil Nagar
- R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Maharashtra, India
| | - Mahmoud E S Soliman
- Molecular Bio-Computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Deepak Lokwani
- R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Maharashtra, India
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Farahani FV, Fiok K, Lahijanian B, Karwowski W, Douglas PK. Explainable AI: A review of applications to neuroimaging data. Front Neurosci 2022; 16:906290. [PMID: 36583102 PMCID: PMC9793854 DOI: 10.3389/fnins.2022.906290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
Deep neural networks (DNNs) have transformed the field of computer vision and currently constitute some of the best models for representations learned via hierarchical processing in the human brain. In medical imaging, these models have shown human-level performance and even higher in the early diagnosis of a wide range of diseases. However, the goal is often not only to accurately predict group membership or diagnose but also to provide explanations that support the model decision in a context that a human can readily interpret. The limited transparency has hindered the adoption of DNN algorithms across many domains. Numerous explainable artificial intelligence (XAI) techniques have been developed to peer inside the "black box" and make sense of DNN models, taking somewhat divergent approaches. Here, we suggest that these methods may be considered in light of the interpretation goal, including functional or mechanistic interpretations, developing archetypal class instances, or assessing the relevance of certain features or mappings on a trained model in a post-hoc capacity. We then focus on reviewing recent applications of post-hoc relevance techniques as applied to neuroimaging data. Moreover, this article suggests a method for comparing the reliability of XAI methods, especially in deep neural networks, along with their advantages and pitfalls.
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Affiliation(s)
- Farzad V. Farahani
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, United States
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Krzysztof Fiok
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Behshad Lahijanian
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Waldemar Karwowski
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Pamela K. Douglas
- School of Modeling, Simulation, and Training, University of Central Florida, Orlando, FL, United States
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5
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Rudrapal M, Gogoi N, Chetia D, Khan J, Banwas S, Alshehri B, Alaidarous MA, Laddha UD, Khairnar SJ, Walode SG. Repurposing of phytomedicine-derived bioactive compounds with promising anti-SARS-CoV-2 potential: Molecular docking, MD simulation and drug-likeness/ADMET studies. Saudi J Biol Sci 2022; 29:2432-2446. [PMID: 34924801 PMCID: PMC8667520 DOI: 10.1016/j.sjbs.2021.12.018] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 11/28/2021] [Accepted: 12/08/2021] [Indexed: 12/14/2022] Open
Abstract
In view of the potential of traditional plant-based remedies (or phytomedicines) in the management of COVID-19, the present investigation was aimed at finding novel anti-SARS-CoV-2 molecules by in silico screening of bioactive phytochemicals (database) using computational methods and drug repurposing approach. A total of 160 compounds belonging to various phytochemical classes (flavonoids, limonoids, saponins, triterpenoids, steroids etc.) were selected (as initial hits) and screened against three specific therapeutic targets (Mpro/3CLpro, PLpro and RdRp) of SARS-CoV-2 by docking, molecular dynamics simulation and drug-likeness/ADMET studies. From our studies, six phytochemicals were identified as notable ant-SARS-CoV-2 agents (best hit molecules) with promising inhibitory effects effective against protease (Mpro and PLpro) and polymerase (RdRp) enzymes. These compounds are namely, ginsenoside Rg2, saikosaponin A, somniferine, betulinic acid, soyasapogenol C and azadirachtin A. On the basis of binding modes and dynamics studies of protein-ligand intercations, ginsenoside Rg2, saikosaponin A, somniferine were found to be the most potent (in silico) inhibitors potentially active against Mpro, PLpro and RdRp, respectively. The present investigation can be directed towards further experimental studies in order to confirm the anti-SARS-CoV-2 efficacy along with toxicities of identified phytomolecules.
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Affiliation(s)
- Mithun Rudrapal
- Department of Pharmaceutical Chemistry, Rasiklal M. Dhariwal Institute of Pharmaceutical Education and Research, Chinchwad, Pune 411019, Maharashtra, India
| | - Neelutpal Gogoi
- Department of Pharmaceutical Sciences, Dibrugarh University, Dibrugarh 786004, Assam, India
| | - Dipak Chetia
- Department of Pharmaceutical Sciences, Dibrugarh University, Dibrugarh 786004, Assam, India
| | - Johra Khan
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia
- Health and Basic Sciences Research Center, Majmaah University, Al Majmaah 11952, Saudi Arabia
| | - Saeed Banwas
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia
- Health and Basic Sciences Research Center, Majmaah University, Al Majmaah 11952, Saudi Arabia
- Department of Biomedical Sciences, Oregon State University, Corvallis, OR 97331, USA
| | - Bader Alshehri
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia
- Health and Basic Sciences Research Center, Majmaah University, Al Majmaah 11952, Saudi Arabia
| | - Mohammed A. Alaidarous
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia
- Health and Basic Sciences Research Center, Majmaah University, Al Majmaah 11952, Saudi Arabia
| | - Umesh D. Laddha
- MET Institute of Pharmacy, Bhujbal Knowledge City, Adgaon, Nasik 422003, Maharashtra, India
| | - Shubham J. Khairnar
- MET Institute of Pharmacy, Bhujbal Knowledge City, Adgaon, Nasik 422003, Maharashtra, India
| | - Sanjay G. Walode
- Department of Pharmaceutical Chemistry, Rasiklal M. Dhariwal Institute of Pharmaceutical Education and Research, Chinchwad, Pune 411019, Maharashtra, India
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6
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Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare. BIG DATA AND COGNITIVE COMPUTING 2022. [DOI: 10.3390/bdcc6010030] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of ‘Big Data’ because, whenever a drug interaction is investigated, it is typically reported and published in healthcare and clinical pharmacology journals. However, it is crucial to automate the extraction of the interactions taking place between drugs because the medical literature is being published in immense volumes, and it is impossible for healthcare professionals to read and collect all of the investigated DDI reports from these Big Data. To avoid this time-consuming procedure, the Information Extraction (IE) and Relationship Extraction (RE) techniques that have been studied in depth in Natural Language Processing (NLP) could be very promising. Since 2011, a lot of research has been reported in this particular area, and there are many approaches that have been implemented that can also be applied to biomedical texts to extract DDI-related information. A benchmark corpus is also publicly available for the advancement of DDI extraction tasks. The current state-of-the-art implementations for extracting DDIs from biomedical texts has employed Support Vector Machines (SVM) or other machine learning methods that work on manually defined features and that might be the cause of the low precision and recall that have been achieved in this domain so far. Modern deep learning techniques have also been applied for the automatic extraction of DDIs from the scientific literature and have proven to be very promising for the advancement of DDI extraction tasks. As such, it is pertinent to investigate deep learning techniques for the extraction and classification of DDIs in order for them to be used in the smart healthcare domain. We proposed a deep neural network-based method (SEV-DDI: Severity-Drug–Drug Interaction) with some further-integrated units/layers to achieve higher precision and accuracy. After successfully outperforming other methods in the DDI classification task, we moved a step further and utilized the methods in a sentiment analysis task to investigate the severity of an interaction. The ability to determine the severity of a DDI will be very helpful for clinical decision support systems in making more accurate and informed decisions, ensuring the safety of the patients.
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Kang M, Ko E, Mersha TB. A roadmap for multi-omics data integration using deep learning. Brief Bioinform 2022; 23:bbab454. [PMID: 34791014 PMCID: PMC8769688 DOI: 10.1093/bib/bbab454] [Citation(s) in RCA: 81] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/30/2021] [Accepted: 10/05/2021] [Indexed: 12/18/2022] Open
Abstract
High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data have revolutionized biomedical research by providing a more comprehensive understanding of the biological systems and molecular mechanisms of disease development. Recently, deep learning (DL) algorithms have become one of the most promising methods in multi-omics data analysis, due to their predictive performance and capability of capturing nonlinear and hierarchical features. While integrating and translating multi-omics data into useful functional insights remain the biggest bottleneck, there is a clear trend towards incorporating multi-omics analysis in biomedical research to help explain the complex relationships between molecular layers. Multi-omics data have a role to improve prevention, early detection and prediction; monitor progression; interpret patterns and endotyping; and design personalized treatments. In this review, we outline a roadmap of multi-omics integration using DL and offer a practical perspective into the advantages, challenges and barriers to the implementation of DL in multi-omics data.
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Affiliation(s)
- Mingon Kang
- Department of Computer Science at the University of Nevada, Las Vegas, NV, USA
| | - Euiseong Ko
- Department of Computer Science at the University of Nevada, Las Vegas, NV, USA
| | - Tesfaye B Mersha
- Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
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Status of Bioinformatics Education in South Asia: Past and Present. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5568262. [PMID: 33997009 PMCID: PMC8096557 DOI: 10.1155/2021/5568262] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/10/2021] [Accepted: 03/26/2021] [Indexed: 11/18/2022]
Abstract
Bioinformatics education has been a hot topic in South Asia, and the interest in this education peaks with the start of the 21st century. The governments of South Asian countries had a systematic effort for bioinformatics. They developed the infrastructures to provide maximum facility to the scientific community to gain maximum output in this field. This article renders bioinformatics, measures, and its importance of implementation in South Asia with proper ways of improving bioinformatics education flaws. It also addresses the problems faced in South Asia and proposes some recommendations regarding bioinformatics education. The information regarding bioinformatics education and institutes was collected from different existing research papers, databases, and surveys. The information was then confirmed by visiting each institution's website, while problems and solutions displayed in the article are mostly in line with South Asian bioinformatics conferences and institutions' objectives. Among South Asian countries, India and Pakistan have developed infrastructure and education regarding bioinformatics rapidly as compared to other countries, whereas Bangladesh, Sri Lanka, and Nepal are still in a progressing phase in this field. To advance in a different sector, the bioinformatics industry has to be revolutionized, and it will contribute to strengthening the pharmaceutical, agricultural, and molecular sectors in South Asia. To advance in bioinformatics, universities' infrastructure needs to be on a par with the current international standards, which will produce well-trained professionals with skills in multiple fields like biotechnology, mathematics, statistics, and computer science. The bioinformatics industry has revolutionized and strengthened the pharmaceutical, agricultural, and molecular sectors in South Asia, and it will serve as the standard of education increases in the South Asian countries. A framework for developing a centralized database is suggested after the literature review to collect and store the information on the current status of South Asian bioinformatics education. This will be named as the South Asian Bioinformatics Education Database (SABE). This will provide comprehensive information regarding the bioinformatics in South Asian countries by the country name, the experts of this field, and the university name to explore the top-ranked outputs relevant to queries.
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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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Abstract
The risk factors for food allergy (FA) include both genetic variants and environmental factors. Advances using both candidate-gene association studies and genome-wide approaches have led to the identification of FA-associated genes involved in immune responses and skin barrier functions. Epigenetic changes have also been associated with the risk of FA. In this chapter, we outline current understanding of the genetics, epigenetics and the interplay with environmental risk factors associated with FA. Future studies of gene-environment interactions, gene-gene interactions, and multi-omics integration may help shed light on the mechanisms of FA, and lead to improved diagnostic and treatment strategies.
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Affiliation(s)
- Elisabet Johansson
- Division of Asthma Research, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, 3333 Burnet Avenue, Cincinnati, OH 45229-3026, USA
| | - Tesfaye B Mersha
- Division of Asthma Research, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, 3333 Burnet Avenue, Cincinnati, OH 45229-3026, USA.
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11
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Machine learning towards intelligent systems: applications, challenges, and opportunities. Artif Intell Rev 2021. [DOI: 10.1007/s10462-020-09948-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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12
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Analysis of factors affecting IoT-based smart hospital design. JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2020; 9:67. [PMID: 33532168 PMCID: PMC7689393 DOI: 10.1186/s13677-020-00215-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 11/10/2020] [Indexed: 11/10/2022]
Abstract
Currently, rapidly developing digital technological innovations affect and change the integrated information management processes of all sectors. The high efficiency of these innovations has inevitably pushed the health sector into a digital transformation process to optimize the technologies and methodologies used to optimize healthcare management systems. In this transformation, the Internet of Things (IoT) technology plays an important role, which enables many devices to connect and work together. IoT allows systems to work together using sensors, connection methods, internet protocols, databases, cloud computing, and analytic as infrastructure. In this respect, it is necessary to establish the necessary technical infrastructure and a suitable environment for the development of smart hospitals. This study points out the optimization factors, challenges, available technologies, and opportunities, as well as the system architecture that come about by employing IoT technology in smart hospital environments. In order to do that, the required technical infrastructure is divided into five layers and the system infrastructure, constraints, and methods needed in each layer are specified, which also includes the smart hospital’s dimensions and extent of intelligent computing and real-time big data analytic. As a result of the study, the deficiencies that may arise in each layer for the smart hospital design model and the factors that should be taken into account to eliminate them are explained. It is expected to provide a road map to managers, system developers, and researchers interested in optimization of the design of the smart hospital system.
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Yan Y, Zhang BW, Li XF, Liu Z. List-wise learning to rank biomedical question-answer pairs with deep ranking recursive autoencoders. PLoS One 2020; 15:e0242061. [PMID: 33166367 PMCID: PMC7652278 DOI: 10.1371/journal.pone.0242061] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 10/26/2020] [Indexed: 11/20/2022] Open
Abstract
Biomedical question answering (QA) represents a growing concern among industry and academia due to the crucial impact of biomedical information. When mapping and ranking candidate snippet answers within relevant literature, current QA systems typically refer to information retrieval (IR) techniques: specifically, query processing approaches and ranking models. However, these IR-based approaches are insufficient to consider both syntactic and semantic relatedness and thus cannot formulate accurate natural language answers. Recently, deep learning approaches have become well-known for learning optimal semantic feature representations in natural language processing tasks. In this paper, we present a deep ranking recursive autoencoders (rankingRAE) architecture for ranking question-candidate snippet answer pairs (Q-S) to obtain the most relevant candidate answers for biomedical questions extracted from the potentially relevant documents. In particular, we convert the task of ranking candidate answers to several simultaneous binary classification tasks for determining whether a question and a candidate answer are relevant. The compositional words and their random initialized vectors of concatenated Q-S pairs are fed into recursive autoencoders to learn the optimal semantic representations in an unsupervised way, and their semantic relatedness is classified through supervised learning. Unlike several existing methods to directly choose the top-K candidates with highest probabilities, we take the influence of different ranking results into consideration. Consequently, we define a listwise "ranking error" for loss function computation to penalize inappropriate answer ranking for each question and to eliminate their influence. The proposed architecture is evaluated with respect to the BioASQ 2013-2018 Six-year Biomedical Question Answering benchmarks. Compared with classical IR models, other deep representation models, as well as some state-of-the-art systems for these tasks, the experimental results demonstrate the robustness and effectiveness of rankingRAE.
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Affiliation(s)
- Yan Yan
- Department of Computer Science and Technology, School of Mechanical Electronic and Information Engineering, China University of Mining and Technology Beijing, Beijing, China
| | | | - Xu-Feng Li
- Department of Computer Science and Technology, School of Mechanical Electronic and Information Engineering, China University of Mining and Technology Beijing, Beijing, China
| | - Zhenhan Liu
- Department of Computer Science and Technology, School of Mechanical Electronic and Information Engineering, China University of Mining and Technology Beijing, Beijing, China
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14
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Track Iran's national COVID-19 response committee's major concerns using two-stage unsupervised topic modeling. Int J Med Inform 2020; 145:104309. [PMID: 33181447 PMCID: PMC7609243 DOI: 10.1016/j.ijmedinf.2020.104309] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 09/22/2020] [Accepted: 10/20/2020] [Indexed: 12/20/2022]
Abstract
Background Since the World Health Organization (WHO) declared the COVID-19 as a Public Health Emergency of International Concern (PHEIC) on January 31, 2020, governments have been enfaced with crisis for timely responses. The efficacy of these responses directly depends on the social behaviors of the target society. People react to these actions with respect to the information they received from different channels, such as news and social networks. Thus, analyzing news demonstrates a brief view of the information users received during the outbreak. Methods The raw data used in this study is collected from official news channels of news wires and agencies in Telegram messenger, which exceeds 2,400,000 posts. The posts that are quoted by NCRC’s members are collected, cleaned, and divided into sentences. The topic modeling and tracking are utilized in a two-stage framework, which is customized for this problem to separate miscellaneous sentences from those presenting concerns. The first stage is fed with embedding vectors of sentences where they are grouped by the Mapper algorithm. Sentences belonging to singleton nodes are labeled as miscellaneous sentences. The remained sentences are vectorized, adopting Tf-IDF weighting schema in the second stage and topically modeled by the LDA method. Finally, relevant topics are aligned to the list of policies and actions, named topic themes, that are set up by the NCRC. Results Our results show that major concerns presented in about half of the sentences are (1) PCR lab. test, diagnosis, and screening, (2) Closure of the education system, and (3) awareness actions about washing hands and facial mask usage. Among the eight themes, intra-provincial travel and traffic restrictions, as well as briefing the national and provincial status, are under-presented. The timeline of concerns annotated by the preventive actions illustrates the changes in concerns addressed by NCRC. This timeline shows that although the announcements and public responses are not lagged behind the events, but cannot be considered as timely. Furthermore, the fluctuating series of concerns reveal that the NCRC has not a long-time response map, and members react to the closest announced policy/act. Conclusion The results of our study can be used as a quantitative indicator for evaluating the availability of an on-time public response of Iran’s NCRC during the first three months of the outbreak. Moreover, it can be used in comparative studies to investigate the differences between awareness acts in various countries. Results of our customized-design framework showed that about one-third of the discussions of the NCRC’s members cover miscellaneous topics that must be removed from the data.
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15
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Aguilera-Mendoza L, Marrero-Ponce Y, García-Jacas CR, Chavez E, Beltran JA, Guillen-Ramirez HA, Brizuela CA. Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach. Sci Rep 2020; 10:18074. [PMID: 33093586 PMCID: PMC7583304 DOI: 10.1038/s41598-020-75029-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Accepted: 09/23/2020] [Indexed: 12/15/2022] Open
Abstract
The increasing interest in bioactive peptides with therapeutic potentials has been reflected in a large variety of biological databases published over the last years. However, the knowledge discovery process from these heterogeneous data sources is a nontrivial task, becoming the essence of our research endeavor. Therefore, we devise a unified data model based on molecular similarity networks for representing a chemical reference space of bioactive peptides, having an implicit knowledge that is currently not explicitly accessed in existing biological databases. Indeed, our main contribution is a novel workflow for the automatic construction of such similarity networks, enabling visual graph mining techniques to uncover new insights from the "ocean" of known bioactive peptides. The workflow presented here relies on the following sequential steps: (i) calculation of molecular descriptors by applying statistical and aggregation operators on amino acid property vectors; (ii) a two-stage unsupervised feature selection method to identify an optimized subset of descriptors using the concepts of entropy and mutual information; (iii) generation of sparse networks where nodes represent bioactive peptides, and edges between two nodes denote their pairwise similarity/distance relationships in the defined descriptor space; and (iv) exploratory analysis using visual inspection in combination with clustering and network science techniques. For practical purposes, the proposed workflow has been implemented in our visual analytics software tool ( http://mobiosd-hub.com/starpep/ ), to assist researchers in extracting useful information from an integrated collection of 45120 bioactive peptides, which is one of the largest and most diverse data in its field. Finally, we illustrate the applicability of the proposed workflow for discovering central nodes in molecular similarity networks that may represent a biologically relevant chemical space known to date.
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Affiliation(s)
- Longendri Aguilera-Mendoza
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Baja California, 22860, Mexico
| | - Yovani Marrero-Ponce
- Universidad San Francisco de Quito, Grupo de Medicina Molecular y Traslacional (MeM&T), Escuela de Medicina, Colegio de Ciencias de la Salud (COCSA), Av. Interoceánica Km 12 1/2 y Av. Florencia, 17-1200-841, Quito, Ecuador.
- Grupo GINUMED, Corporacion Universitaria Rafael Nuñez. Facultad de Salud, Programa de Medicina, Cartagena, Colombia.
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia, Spain.
| | - César R García-Jacas
- Cátedras Conacyt - Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada, Baja California, Mexico
| | - Edgar Chavez
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Baja California, 22860, Mexico
| | - Jesus A Beltran
- Department of Informatics, University of California, Irvine, Irvine, CA, USA
| | - Hugo A Guillen-Ramirez
- Department of BioMedical Research (DBMR), University of Bern, Bern, 3008, Switzerland
- Department of Medical Oncology, Inselspital, University Hospital and University of Bern, 3010, Bern, Switzerland
| | - Carlos A Brizuela
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Baja California, 22860, Mexico.
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Interactive clustering: a scoping review. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09913-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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17
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Graf WD, Shprintzen RJ. "Retrofitting" established genetic disorders and diseases through big data and phenomics. Neurol Clin Pract 2020; 10:375-376. [PMID: 33304644 PMCID: PMC7717638 DOI: 10.1212/cpj.0000000000000784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- William D Graf
- Connecticut Children's (WDG), Farmington; and The Virtual Center for Velo-Cardio-Facial Syndrome (RJS), Manlius, NY
| | - Robert J Shprintzen
- Connecticut Children's (WDG), Farmington; and The Virtual Center for Velo-Cardio-Facial Syndrome (RJS), Manlius, NY
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18
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Verkhivker GM, Agajanian S, Hu G, Tao P. Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning. Front Mol Biosci 2020; 7:136. [PMID: 32733918 PMCID: PMC7363947 DOI: 10.3389/fmolb.2020.00136] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 06/08/2020] [Indexed: 12/12/2022] Open
Abstract
Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets.
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Affiliation(s)
- Gennady M. Verkhivker
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA, United States
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, United States
| | - Steve Agajanian
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA, United States
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Peng Tao
- Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), Center for Scientific Computation, Southern Methodist University, Dallas, TX, United States
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19
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Hartmann J, Wong M, Gallo E, Gilmour D. An image-based data-driven analysis of cellular architecture in a developing tissue. eLife 2020; 9:e55913. [PMID: 32501214 PMCID: PMC7274788 DOI: 10.7554/elife.55913] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 05/24/2020] [Indexed: 12/22/2022] Open
Abstract
Quantitative microscopy is becoming increasingly crucial in efforts to disentangle the complexity of organogenesis, yet adoption of the potent new toolbox provided by modern data science has been slow, primarily because it is often not directly applicable to developmental imaging data. We tackle this issue with a newly developed algorithm that uses point cloud-based morphometry to unpack the rich information encoded in 3D image data into a straightforward numerical representation. This enabled us to employ data science tools, including machine learning, to analyze and integrate cell morphology, intracellular organization, gene expression and annotated contextual knowledge. We apply these techniques to construct and explore a quantitative atlas of cellular architecture for the zebrafish posterior lateral line primordium, an experimentally tractable model of complex self-organized organogenesis. In doing so, we are able to retrieve both previously established and novel biologically relevant patterns, demonstrating the potential of our data-driven approach.
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Affiliation(s)
- Jonas Hartmann
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL)HeidelbergGermany
| | - Mie Wong
- Institute of Molecular Life Sciences, University of Zurich (UZH)ZurichSwitzerland
| | - Elisa Gallo
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL)HeidelbergGermany
- Institute of Molecular Life Sciences, University of Zurich (UZH)ZurichSwitzerland
- Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of BiosciencesHeidelbergGermany
| | - Darren Gilmour
- Institute of Molecular Life Sciences, University of Zurich (UZH)ZurichSwitzerland
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20
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Resolving Clinical Phenotypes into Endotypes in Allergy: Molecular and Omics Approaches. Clin Rev Allergy Immunol 2020; 60:200-219. [PMID: 32378146 DOI: 10.1007/s12016-020-08787-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Allergic diseases are highly complex with respect to pathogenesis, inflammation, and response to treatment. Current efforts for allergic disease diagnosis have focused on clinical evidence as a binary outcome. Although outcome status based on clinical phenotypes (observable characteristics) is convenient and inexpensive to measure in large studies, it does not adequately provide insight into the complex molecular determinants of allergic disease. Individuals with similar clinical diagnoses do not necessarily have similar disease etiologies, natural histories, or responses to treatment. This heterogeneity contributes to the ineffective response to treatment leading to an annual estimated cost of $350 billion in the USA alone. There has been a recent focus to deconvolute the clinical heterogeneity of allergic diseases into specific endotypes using molecular and omics approaches. Endotypes are a means to classify patients based on the underlying pathophysiological mechanisms involving distinct functions or treatment response. The advent of high-throughput molecular omics, immunophenotyping, and bioinformatics methods including machine learning algorithms is facilitating the development of endotype-based diagnosis. As we move to the next decade, we should truly start treating clinical endotypes not clinical phenotype. This review highlights current efforts taking place to improve allergic disease endotyping via molecular omics profiling, immunophenotyping, and machine learning approaches in the context of precision diagnostics in allergic diseases. Graphical Abstract.
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21
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Larijani B, Goodarzi P, Sheikh Hosseini M, M. Nejad S, Alavi-Moghadam S, Sarvari M, Abedi M, Arabi M, Rahim F, Foroughi Heravani N, Hadavandkhani M, Payab M. OMICs Profiling of Cancer Cells. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/978-3-030-27727-7_8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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22
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Vizán-Rico HI, Mayer C, Petersen M, McKenna DD, Zhou X, Gómez-Zurita J. Patterns and Constraints in the Evolution of Sperm Individualization Genes in Insects, with an Emphasis on Beetles. Genes (Basel) 2019; 10:E776. [PMID: 31590243 PMCID: PMC6826512 DOI: 10.3390/genes10100776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Revised: 09/20/2019] [Accepted: 10/01/2019] [Indexed: 11/17/2022] Open
Abstract
Gene expression profiles can change dramatically between sexes and sex bias may contribute specific macroevolutionary dynamics for sex-biased genes. However, these dynamics are poorly understood at large evolutionary scales due to the paucity of studies that have assessed orthology and functional homology for sex-biased genes and the pleiotropic effects possibly constraining their evolutionary potential. Here, we explore the correlation of sex-biased expression with macroevolutionary processes that are associated with sex-biased genes, including duplications and accelerated evolutionary rates. Specifically, we examined these traits in a group of 44 genes that orchestrate sperm individualization during spermatogenesis, with both unbiased and sex-biased expression. We studied these genes in the broad evolutionary framework of the Insecta, with a particular focus on beetles (order Coleoptera). We studied data mined from 119 insect genomes, including 6 beetle models, and from 19 additional beetle transcriptomes. For the subset of physically and/or genetically interacting proteins, we also analyzed how their network structure may condition the mode of gene evolution. The collection of genes was highly heterogeneous in duplication status, evolutionary rates, and rate stability, but there was statistical evidence for sex bias correlated with faster evolutionary rates, consistent with theoretical predictions. Faster rates were also correlated with clocklike (insect amino acids) and non-clocklike (beetle nucleotides) substitution patterns in these genes. Statistical associations (higher rates for central nodes) or lack thereof (centrality of duplicated genes) were in contrast to some current evolutionary hypotheses, highlighting the need for more research on these topics.
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Affiliation(s)
- Helena I. Vizán-Rico
- Animal Biodiversity and Evolution, Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Spain;
| | - Christoph Mayer
- Center for Molecular Biodiversity Research, Zoological Research Museum Alexander Koenig, 53113 Bonn, Germany; (C.M.); (M.P.)
| | - Malte Petersen
- Center for Molecular Biodiversity Research, Zoological Research Museum Alexander Koenig, 53113 Bonn, Germany; (C.M.); (M.P.)
| | - Duane D. McKenna
- Center for Biodiversity Research, Department of Biological Sciences, University of Memphis, Memphis, TN 38152, USA;
| | - Xin Zhou
- Department of Entomology, College of Plant Protection, China Agricultural University, Beijing 100193, China;
| | - Jesús Gómez-Zurita
- Animal Biodiversity and Evolution, Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Spain;
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23
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Holzinger A, Langs G, Denk H, Zatloukal K, Müller H. Causability and explainability of artificial intelligence in medicine. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2019; 9:e1312. [PMID: 32089788 PMCID: PMC7017860 DOI: 10.1002/widm.1312] [Citation(s) in RCA: 305] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 01/26/2019] [Accepted: 02/24/2019] [Indexed: 05/02/2023]
Abstract
Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. However, their weakness was in dealing with uncertainties of the real world. Through the introduction of probabilistic learning, applications became increasingly successful, but increasingly opaque. Explainable AI deals with the implementation of transparency and traceability of statistical black-box machine learning methods, particularly deep learning (DL). We argue that there is a need to go beyond explainable AI. To reach a level of explainable medicine we need causability. In the same way that usability encompasses measurements for the quality of use, causability encompasses measurements for the quality of explanations. In this article, we provide some necessary definitions to discriminate between explainability and causability as well as a use-case of DL interpretation and of human explanation in histopathology. The main contribution of this article is the notion of causability, which is differentiated from explainability in that causability is a property of a person, while explainability is a property of a system This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction.
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Affiliation(s)
- Andreas Holzinger
- Institute for Medical Informatics, Statistics and DocumentationMedical University GrazGrazAustria
| | - Georg Langs
- Department of Biomedical Imaging and Image‐guided TherapyComputational Imaging Research Lab, Medical University of ViennaViennaAustria
| | - Helmut Denk
- Institute of PathologyMedical University GrazGrazAustria
| | - Kurt Zatloukal
- Institute of PathologyMedical University GrazGrazAustria
| | - Heimo Müller
- Institute for Medical Informatics, Statistics and DocumentationMedical University GrazGrazAustria
- Institute of PathologyMedical University GrazGrazAustria
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24
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DiiS: A Biomedical Data Access Framework for Aiding Data Driven Research Supporting FAIR Principles. DATA 2019. [DOI: 10.3390/data4020054] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Vast amounts of clinical and biomedical research data are produced daily. These data can help enable data driven healthcare through novel biomedical discoveries, improved diagnostics processes, epidemiology, and education. However, finding, and gaining access to these data and relevant metadata that are necessary to achieve these goals remains a challenge. Furthermore, data management and enabling widespread, albeit controlled, use poses a major challenge for data producers. These data sources are often geographically distributed, with diverse characteristics, and are controlled by a host of logistical and legal factors that require appropriate governance and access control guarantees. To overcome these obstacles, a set of guiding principles under the term FAIR has been previously introduced. The primary desirable dataset properties are thus that the data should be Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we introduce and describe an abstract framework that models these ideal goals, and could be a step toward supporting data driven research. We also develop a system instantiated on our framework called the Data integration and indexing System (DiiS). The system provides an integration model for making healthcare data available on a global scale. Our research work describes the challenges inhibiting data producers, data stewards, and data brokers in achieving FAIR goals for sharing biomedical data. We attempt to address some of the key challenges through the proposed system. We evaluated our framework using the software architecture testing technique and also looked at how different challenges in data integration are addressed by our system. Our evaluation shows that the DiiS framework is a user friendly data integration system that would greatly contribute to biomedical research.
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25
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O'Sullivan S, Heinsen H, Grinberg LT, Chimelli L, Amaro E, do Nascimento Saldiva PH, Jeanquartier F, Jean-Quartier C, da Graça Morais Martin M, Sajid MI, Holzinger A. The role of artificial intelligence and machine learning in harmonization of high-resolution post-mortem MRI (virtopsy) with respect to brain microstructure. Brain Inform 2019; 6:3. [PMID: 30843118 PMCID: PMC6403267 DOI: 10.1186/s40708-019-0096-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Accepted: 01/16/2019] [Indexed: 02/06/2023] Open
Abstract
Enhanced resolution of 7 T magnetic resonance imaging (MRI) scanners has considerably advanced our knowledge of structure and function in human and animal brains. Post-industrialized countries are particularly prone to an ever-increasing number of ageing individuals and ageing-associated neurodegenerative diseases. Neurodegenerative diseases are associated with volume loss in the affected brain. MRI diagnoses and monitoring of subtle volume changes in the ageing/diseased brains have the potential to become standard diagnostic tools. Even with the superior resolution of 7 T MRI scanners, the microstructural changes comprising cell types, cell numbers, and cellular processes, are still undetectable. Knowledge of origin, nature, and progression for microstructural changes are necessary to understand pathogenetic stages in the relentless neurodegenerative diseases, as well as to develop therapeutic tools that delay or stop neurodegenerative processes at their earliest stage. We illustrate the gap in resolution by comparing the identical regions of the post-mortem in situ 7 T MR images (virtual autopsy or virtopsy) with the histological observations in serial sections through the same brain. We also described the protocols and limitations associated with these comparisons, as well as the necessity of supercomputers and data management for "Big data". Analysis of neuron and/or glial number by using a body of mathematical tools and guidelines (stereology) is time-consuming, cumbersome, and still restricted to trained human investigators. Development of tools based on machine learning (ML) and artificial intelligence (AI) could considerably accelerate studies on localization, onset, and progression of neuron loss. Finally, these observations could disentangle the mechanisms of volume loss into stages of reversible atrophy and/or irreversible fatal cell death. This AI- and ML-based cooperation between virtopsy and histology could bridge the present gap between virtual reality and neuropathology. It could also culminate in the creation of an imaging-associated comprehensive database. This database would include genetic, clinical, epidemiological, and technical aspects that could help to alleviate or even stop the adverse effects of neurodegenerative diseases on affected individuals, their families, and society.
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Affiliation(s)
- Shane O'Sullivan
- Department of Pathology, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil.
| | - Helmut Heinsen
- Department of Pathology, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil.,Morphological Brain Research Unit, University of Würzburg, Würzburg, Germany.,Institute of Radiology, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil
| | - Lea Tenenholz Grinberg
- Morphological Brain Research Unit, University of Würzburg, Würzburg, Germany.,Aging Brain Project, Department of Pathology, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil.,Albert Einstein Instituto Israelita de Ensino e Pesquisa, São Paulo, Brazil
| | - Leila Chimelli
- Laboratory of Neuropathology, State Institute of Brain, Rio de Janeiro, Brazil
| | - Edson Amaro
- Institute of Radiology, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil
| | - Paulo Hilário do Nascimento Saldiva
- Department of Pathology, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil.,Institute of Advanced Studies, Universidade de Sao Paulo, São Paulo, Brazil
| | - Fleur Jeanquartier
- Holzinger Group, Institute for Medical Informatics and Statistics, Medical University of Graz, Graz, Austria
| | - Claire Jean-Quartier
- Holzinger Group, Institute for Medical Informatics and Statistics, Medical University of Graz, Graz, Austria
| | | | - Mohammed Imran Sajid
- Department of Upper GI Surgery, Wirral University Teaching Hospital, Birkenhead, United Kingdom
| | - Andreas Holzinger
- Holzinger Group, Institute for Medical Informatics and Statistics, Medical University of Graz, Graz, Austria
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Chen GM, Pather SR, DeLisser HM. Trends in the Use of Common Words and Patient-Centric Language in the Titles of Medical Journals, 1976-2015. JAMA Netw Open 2019; 2:e191083. [PMID: 30901051 PMCID: PMC6583309 DOI: 10.1001/jamanetworkopen.2019.1083] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 01/29/2019] [Indexed: 01/12/2023] Open
Abstract
Importance The language of medical research appears to be intrinsically tied to the culture of medical research and provides a unique window into broader trends in the culture of medicine. Objective To analyze medical language from 5 premier medical journals and investigate broader changes in the culture of clinical investigation during the last 40 years. Design, Setting, and Participants In this qualitative study using a data-driven analysis, 302 293 PubMed records were extracted from JAMA, The Lancet, Annals of Internal Medicine, the BMJ, and New England Journal of Medicine from January 1, 1976, through December 31, 2015, to identify key trends in medical language. A frequency analysis was applied across the 40-year time frame in JAMA to assess the major trends in all publication types. Patient-centered language was analyzed in clinical trials in the flanking time periods (1976-1980 and 2011-2015) across the 5 journals. Data were analyzed from November 16, 2016, through November 9, 2018. Main Outcomes and Measures Increasing or decreasing frequency of words (monograms) and word pairs (bigrams) and the proportion of patient-centric words in journal article titles. Results In JAMA, 50 277 articles of all publication types were included. In the frequency analysis, the most increased terms were reflective of the language of epidemiological research. The bigram analysis revealed a decline in causal language (-2.42/100 000 words to -2.03/100 000 words; false discovery rate [FDR], <0.01) and an increased description of patients in the plural form (6.92/100 000 words to 11.4/100 000 words; FDR, <0.01). A trend to separate patient from disease was observed; for example, there was a decrease in describing a patient as a diabetic (-2.21/100 000 words; FDR, <0.01) compared with a patient with diabetes. In the analysis of clinical trials in all 5 journals, 3125 titles were identified (range, 193-932 per journal). In 4 of the 5 journals, use of patient-centric keywords increased significantly (absolute increase, 18.9%-34.3%; P < .001 for 3 journals; P = .01 for 1 journal), with the New England Journal of Medicine as the exception. This finding reflects a change from shorter disease-centric titles to longer titles that describe patients with a disease. Conclusions and Relevance Trends in medical language reflect the rise of evidence-based medicine, a shift in focus from individuals to populations, and a separation of patient and disease. Data-driven analysis of medical language provides a unique window into the changing landscape of medical culture.
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Affiliation(s)
- Gregory M. Chen
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Sarshan R. Pather
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Graduate Group in Cell and Molecular Biology, University of Pennsylvania, Philadelphia
| | - Horace M. DeLisser
- Division of Pulmonary, Allergy and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Academic Programs, Jordan Medical Education Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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Vegter MW. Towards precision medicine; a new biomedical cosmology. MEDICINE, HEALTH CARE, AND PHILOSOPHY 2018; 21:443-456. [PMID: 29429062 PMCID: PMC6267256 DOI: 10.1007/s11019-018-9828-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Precision Medicine has become a common label for data-intensive and patient-driven biomedical research. Its intended future is reflected in endeavours such as the Precision Medicine Initiative in the USA. This article addresses the question whether it is possible to discern a new 'medical cosmology' in Precision Medicine, a concept that was developed by Nicholas Jewson to describe comprehensive transformations involving various dimensions of biomedical knowledge and practice, such as vocabularies, the roles of patients and physicians and the conceptualisation of disease. Subsequently, I will elaborate my assessment of the features of Precision Medicine with the help of Michel Foucault, by exploring how precision medicine involves a transformation along three axes: the axis of biomedical knowledge, of biomedical power and of the patient as a self. Patients are encouraged to become the managers of their own health status, while the medical domain is reframed as a data-sharing community, characterised by changing power relationships between providers and patients, producers and consumers. While the emerging Precision Medicine cosmology may surpass existing knowledge frameworks; it obscures previous traditions and reduces research-subjects to mere data. This in turn, means that the individual is both subjected to the neoliberal demand to share personal information, and at the same time has acquired the positive 'right' to become a member of the data-sharing community. The subject has to constantly negotiate the meaning of his or her data, which can either enable self-expression, or function as a commanding Superego.
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Affiliation(s)
- M W Vegter
- Faculty of Science, Institute for Science in Society, Radboud University Nijmegen, P.O. Box 9010, 6500 GL, Nijmegen, The Netherlands.
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Abrams ZB, Zucker M, Wang M, Asiaee Taheri A, Abruzzo LV, Coombes KR. Thirty biologically interpretable clusters of transcription factors distinguish cancer type. BMC Genomics 2018; 19:738. [PMID: 30305013 PMCID: PMC6180590 DOI: 10.1186/s12864-018-5093-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Accepted: 09/19/2018] [Indexed: 12/27/2022] Open
Abstract
Background Transcription factors are essential regulators of gene expression and play critical roles in development, differentiation, and in many cancers. To carry out their regulatory programs, they must cooperate in networks and bind simultaneously to sites in promoter or enhancer regions of genes. We hypothesize that the mRNA co-expression patterns of transcription factors can be used both to learn how they cooperate in networks and to distinguish between cancer types. Results We recently developed a new algorithm, Thresher, that combines principal component analysis, outlier filtering, and von Mises-Fisher mixture models to cluster genes (in this case, transcription factors) based on expression, determining the optimal number of clusters in the process. We applied Thresher to the RNA-Seq expression data of 486 transcription factors from more than 10,000 samples of 33 kinds of cancer studied in The Cancer Genome Atlas (TCGA). We found that 30 clusters of transcription factors from a 29-dimensional principal component space were able to distinguish between most cancer types, and could separate tumor samples from normal controls. Moreover, each cluster of transcription factors could be either (i) linked to a tissue-specific expression pattern or (ii) associated with a fundamental biological process such as cell cycle, angiogenesis, apoptosis, or cytoskeleton. Clusters of the second type were more likely also to be associated with embryonically lethal mouse phenotypes. Conclusions Using our approach, we have shown that the mRNA expression patterns of transcription factors contain most of the information needed to distinguish different cancer types. The Thresher method is capable of discovering biologically interpretable clusters of genes. It can potentially be applied to other gene sets, such as signaling pathways, to decompose them into simpler, yet biologically meaningful, components. Electronic supplementary material The online version of this article (10.1186/s12864-018-5093-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zachary B Abrams
- Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, 43210, OH, USA
| | - Mark Zucker
- Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, 43210, OH, USA
| | - Min Wang
- Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, 43210, OH, USA.,Mathematical Biosciences Institute, The Ohio State University, 1735 Neil Avenue, Columbus, 43210, OH, USA
| | - Amir Asiaee Taheri
- Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, 43210, OH, USA.,Mathematical Biosciences Institute, The Ohio State University, 1735 Neil Avenue, Columbus, 43210, OH, USA
| | - Lynne V Abruzzo
- Department of Pathology, The Ohio State University, 129 Hamilton Hall, 1645 Neil Avenue, Columbus, 43210, OH, USA
| | - Kevin R Coombes
- Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, 43210, OH, USA.
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A Machine Learning Perspective on Personalized Medicine: An Automized, Comprehensive Knowledge Base with Ontology for Pattern Recognition. MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2018. [DOI: 10.3390/make1010009] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Personalized or precision medicine is a new paradigm that holds great promise for individualized patient diagnosis, treatment, and care. However, personalized medicine has only been described on an informal level rather than through rigorous practical guidelines and statistical protocols that would allow its robust practical realization for implementation in day-to-day clinical practice. In this paper, we discuss three key factors, which we consider dimensions that effect the experimental design for personalized medicine: (I) phenotype categories; (II) population size; and (III) statistical analysis. This formalization allows us to define personalized medicine from a machine learning perspective, as an automized, comprehensive knowledge base with an ontology that performs pattern recognition of patient profiles.
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30
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Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations? MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2018. [DOI: 10.3390/make1010008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Causal networks, e.g., gene regulatory networks (GRNs) inferred from gene expression data, contain a wealth of information but are defying simple, straightforward and low-budget experimental validations. In this paper, we elaborate on this problem and discuss distinctions between biological and clinical validations. As a result, validation differences for GRNs reflect known differences between basic biological and clinical research questions making the validations context specific. Hence, the meaning of biologically and clinically meaningful GRNs can be very different. For a concerted approach to a problem of this size, we suggest the establishment of the HUMAN GENE REGULATORY NETWORK PROJECT which provides the information required for biological and clinical validations alike.
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Glouzon JPS, Perreault JP, Wang S. Structurexplor: a platform for the exploration of structural features of RNA secondary structures. Bioinformatics 2018; 33:3117-3120. [PMID: 28575203 DOI: 10.1093/bioinformatics/btx323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 05/26/2017] [Indexed: 11/14/2022] Open
Abstract
Summary Discovering function-related structural features, such as the cloverleaf shape of transfer RNA secondary structures, is essential to understand RNA function. With this aim, we have developed a platform, named Structurexplor, to facilitate the exploration of structural features in populations of RNA secondary structures. It has been designed and developed to help biologists interactively search for, evaluate and select interesting structural features that can potentially explain RNA functions. Availability and implementation Structurxplor is a web application available at http://structurexplor.dinf.usherbrooke.ca. The source code can be found at http://jpsglouzon.github.io/structurexplor/. Contact shengrui.wang@usherbrooke.ca. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jean-Pierre Séhi Glouzon
- Department of Computer Science, Faculty of Science, Université de Sherbrooke, Sherbrooke, QC, J1K 2R1 Canada.,RNA Group, Department of Biochemistry, Faculty of Medicine and Health Sciences, Applied Cancer Research Pavilion, Université de Sherbrooke, Sherbrooke, QC, J1K 2R1, Canada
| | - Jean-Pierre Perreault
- RNA Group, Department of Biochemistry, Faculty of Medicine and Health Sciences, Applied Cancer Research Pavilion, Université de Sherbrooke, Sherbrooke, QC, J1K 2R1, Canada
| | - Shengrui Wang
- Department of Computer Science, Faculty of Science, Université de Sherbrooke, Sherbrooke, QC, J1K 2R1 Canada
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32
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A Survey of Data Mining and Deep Learning in Bioinformatics. J Med Syst 2018; 42:139. [DOI: 10.1007/s10916-018-1003-9] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 06/21/2018] [Indexed: 12/13/2022]
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33
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Zafeiris D, Rutella S, Ball GR. An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study. Comput Struct Biotechnol J 2018; 16:77-87. [PMID: 29977480 PMCID: PMC6026215 DOI: 10.1016/j.csbj.2018.02.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Revised: 02/06/2018] [Accepted: 02/11/2018] [Indexed: 12/15/2022] Open
Abstract
The field of machine learning has allowed researchers to generate and analyse vast amounts of data using a wide variety of methodologies. Artificial Neural Networks (ANN) are some of the most commonly used statistical models and have been successful in biomarker discovery studies in multiple disease types. This review seeks to explore and evaluate an integrated ANN pipeline for biomarker discovery and validation in Alzheimer's disease, the most common form of dementia worldwide with no proven cause and no available cure. The proposed pipeline consists of analysing public data with a categorical and continuous stepwise algorithm and further examination through network inference to predict gene interactions. This methodology can reliably generate novel markers and further examine known ones and can be used to guide future research in Alzheimer's disease.
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Affiliation(s)
- Dimitrios Zafeiris
- John van Geest Cancer Research Centre, College of Science and Technology, Nottingham Trent University, United Kingdom
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34
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Deng W, Papavasileiou I, Qiao Z, Zhang W, Lam KY, Han S. Advances in Automation Technologies for Lower Extremity Neurorehabilitation: A Review and Future Challenges. IEEE Rev Biomed Eng 2018; 11:289-305. [DOI: 10.1109/rbme.2018.2830805] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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35
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Nayak C, Chandra I, Singh P, Singh SK. Omics-Based Nanomedicine. Synth Biol (Oxf) 2018. [DOI: 10.1007/978-981-10-8693-9_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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36
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Plaza F, Salas R, Yáñez E. Identifying ecosystem patterns from time series of anchovy (Engraulis ringens) and sardine (Sardinops sagax) landings in northern Chile. J STAT COMPUT SIM 2017. [DOI: 10.1080/00949655.2017.1410150] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
| | - Rodrigo Salas
- Biomedical Engineering School, Engineering Faculty, Universidad de Valparaíso, Valparaíso, Chile
| | - Eleuterio Yáñez
- Marine Sciences School, Natural Resources Faculty, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
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37
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Studying protein-protein interactions: progress, pitfalls and solutions. Biochem Soc Trans 2017; 44:994-1004. [PMID: 27528744 DOI: 10.1042/bst20160092] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Indexed: 12/27/2022]
Abstract
Signalling proteins are intrinsic to all biological processes and interact with each other in tightly regulated and orchestrated signalling complexes and pathways. Characterization of protein binding can help to elucidate protein function within signalling pathways. This information is vital for researchers to gain a more comprehensive knowledge of cellular networks which can then be used to develop new therapeutic strategies for disease. However, studying protein-protein interactions (PPIs) can be challenging as the interactions can be extremely transient downstream of specific environmental cues. There are many powerful techniques currently available to identify and confirm PPIs. Choosing the most appropriate range of techniques merits serious consideration. The aim of this review is to provide a starting point for researchers embarking on a PPI study. We provide an overview and point of reference for some of the many methods available to identify interactions from in silico analysis and large scale screening tools through to the methods used to validate potential PPIs. We discuss the advantages and disadvantages of each method and we also provide a workflow chart to highlight the main experimental questions to consider when planning cell lysis to maximize experimental success.
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Lee H, Kim S, Kim JW, Chung YD. Utility-preserving anonymization for health data publishing. BMC Med Inform Decis Mak 2017; 17:104. [PMID: 28693480 PMCID: PMC5504813 DOI: 10.1186/s12911-017-0499-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Accepted: 06/28/2017] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Publishing raw electronic health records (EHRs) may be considered as a breach of the privacy of individuals because they usually contain sensitive information. A common practice for the privacy-preserving data publishing is to anonymize the data before publishing, and thus satisfy privacy models such as k-anonymity. Among various anonymization techniques, generalization is the most commonly used in medical/health data processing. Generalization inevitably causes information loss, and thus, various methods have been proposed to reduce information loss. However, existing generalization-based data anonymization methods cannot avoid excessive information loss and preserve data utility. METHODS We propose a utility-preserving anonymization for privacy preserving data publishing (PPDP). To preserve data utility, the proposed method comprises three parts: (1) utility-preserving model, (2) counterfeit record insertion, (3) catalog of the counterfeit records. We also propose an anonymization algorithm using the proposed method. Our anonymization algorithm applies full-domain generalization algorithm. We evaluate our method in comparison with existence method on two aspects, information loss measured through various quality metrics and error rate of analysis result. RESULTS With all different types of quality metrics, our proposed method show the lower information loss than the existing method. In the real-world EHRs analysis, analysis results show small portion of error between the anonymized data through the proposed method and original data. CONCLUSIONS We propose a new utility-preserving anonymization method and an anonymization algorithm using the proposed method. Through experiments on various datasets, we show that the utility of EHRs anonymized by the proposed method is significantly better than those anonymized by previous approaches.
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Affiliation(s)
- Hyukki Lee
- Department of Computer Science and Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841 Republic of Korea
| | - Soohyung Kim
- Department of IT Convegence, Korea University, Seoul, 145 Anam-ro, Seongbuk-gu, 02841 Republic of Korea
| | - Jong Wook Kim
- Department of Media Software, Seoul, 20-Gil, Hongji-dong, Seongbuk-gu, 03016 Republic of Korea
| | - Yon Dohn Chung
- Department of Computer Science and Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841 Republic of Korea
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Introduction to MAchine Learning & Knowledge Extraction (MAKE). MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2017. [DOI: 10.3390/make1010001] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The grand goal of Machine Learning is to develop software which can learn from previous experience—similar to how we humans do. Ultimately, to reach a level of usable intelligence, we need (1) to learn from prior data, (2) to extract knowledge, (3) to generalize—i.e., guessing where probability function mass/density concentrates, (4) to fight the curse of dimensionality, and (5) to disentangle underlying explanatory factors of the data—i.e., to make sense of the data in the context of an application domain. To address these challenges and to ensure successful machine learning applications in various domains an integrated machine learning approach is important. This requires a concerted international effort without boundaries, supporting collaborative, cross-domain, interdisciplinary and transdisciplinary work of experts from seven sections, ranging from data pre-processing to data visualization, i.e., to map results found in arbitrarily high dimensional spaces into the lower dimensions to make it accessible, usable and useful to the end user. An integrated machine learning approach needs also to consider issues of privacy, data protection, safety, security, user acceptance and social implications. This paper is the inaugural introduction to the new journal of MAchine Learning & Knowledge Extraction (MAKE). The goal is to provide an incomplete, personally biased, but consistent introduction into the concepts of MAKE and a brief overview of some selected topics to stimulate future research in the international research community.
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A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells. PLoS One 2017; 12:e0179613. [PMID: 28654683 PMCID: PMC5487013 DOI: 10.1371/journal.pone.0179613] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 05/31/2017] [Indexed: 12/16/2022] Open
Abstract
The development of high-throughput biomolecular technologies has resulted in generation of vast omics data at an unprecedented rate. This is transforming biomedical research into a big data discipline, where the main challenges relate to the analysis and interpretation of data into new biological knowledge. The aim of this study was to develop a framework for biomedical big data analytics, and apply it for analyzing transcriptomics time series data from early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. To this end, transcriptome profiling by microarray was performed on differentiating human pluripotent stem cells sampled at eleven consecutive days. The gene expression data was analyzed using the five-stage analysis framework proposed in this study, including data preparation, exploratory data analysis, confirmatory analysis, biological knowledge discovery, and visualization of the results. Clustering analysis revealed several distinct expression profiles during differentiation. Genes with an early transient response were strongly related to embryonic- and mesendoderm development, for example CER1 and NODAL. Pluripotency genes, such as NANOG and SOX2, exhibited substantial downregulation shortly after onset of differentiation. Rapid induction of genes related to metal ion response, cardiac tissue development, and muscle contraction were observed around day five and six. Several transcription factors were identified as potential regulators of these processes, e.g. POU1F1, TCF4 and TBP for muscle contraction genes. Pathway analysis revealed temporal activity of several signaling pathways, for example the inhibition of WNT signaling on day 2 and its reactivation on day 4. This study provides a comprehensive characterization of biological events and key regulators of the early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. The proposed analysis framework can be used to structure data analysis in future research, both in stem cell differentiation, and more generally, in biomedical big data analytics.
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Berrouiguet S, Barrigón ML, Brandt SA, Nitzburg GC, Ovejero S, Alvarez-Garcia R, Carballo J, Walter M, Billot R, Lenca P, Delgado-Gomez D, Ropars J, de la Calle Gonzalez I, Courtet P, Baca-García E. Ecological Assessment of Clinicians' Antipsychotic Prescription Habits in Psychiatric Inpatients: A Novel Web- and Mobile Phone-Based Prototype for a Dynamic Clinical Decision Support System. J Med Internet Res 2017; 19:e25. [PMID: 28126703 PMCID: PMC5301080 DOI: 10.2196/jmir.5954] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2016] [Revised: 08/25/2016] [Accepted: 08/26/2016] [Indexed: 11/13/2022] Open
Abstract
Background Electronic prescribing devices with clinical decision support systems (CDSSs) hold the potential to significantly improve pharmacological treatment management. Objective The aim of our study was to develop a novel Web- and mobile phone–based application to provide a dynamic CDSS by monitoring and analyzing practitioners’ antipsychotic prescription habits and simultaneously linking these data to inpatients’ symptom changes. Methods We recruited 353 psychiatric inpatients whose symptom levels and prescribed medications were inputted into the MEmind application. We standardized all medications in the MEmind database using the Anatomical Therapeutic Chemical (ATC) classification system and the defined daily dose (DDD). For each patient, MEmind calculated an average for the daily dose prescribed for antipsychotics (using the N05A ATC code), prescribed daily dose (PDD), and the PDD to DDD ratio. Results MEmind results found that antipsychotics were used by 61.5% (217/353) of inpatients, with the largest proportion being patients with schizophrenia spectrum disorders (33.4%, 118/353). Of the 217 patients, 137 (63.2%, 137/217) were administered pharmacological monotherapy and 80 (36.8%, 80/217) were administered polytherapy. Antipsychotics were used mostly in schizophrenia spectrum and related psychotic disorders, but they were also prescribed in other nonpsychotic diagnoses. Notably, we observed polypharmacy going against current antipsychotics guidelines. Conclusions MEmind data indicated that antipsychotic polypharmacy and off-label use in inpatient units is commonly practiced. MEmind holds the potential to create a dynamic CDSS that provides real-time tracking of prescription practices and symptom change. Such feedback can help practitioners determine a maximally therapeutic drug treatment while avoiding unproductive overprescription and off-label use.
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Affiliation(s)
- Sofian Berrouiguet
- Department of Psychiatry, Brest Medical University Hospital at Brest, Brest, France.,UMR CNRS 6285 Lab-STICC, Institut Mines-Telecom, Brest, France.,ERCR SPURBO, Université de Bretagne occidentale, Brest, France
| | | | - Sara A Brandt
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - George C Nitzburg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Santiago Ovejero
- Department of Psychiatry, Fundacion Jimenez Diaz Hospital, Madrid, Spain
| | | | - Juan Carballo
- Department of Psychiatry, Fundacion Jimenez Diaz Hospital, Madrid, Spain
| | - Michel Walter
- Department of Psychiatry, Brest Medical University Hospital at Brest, Brest, France
| | - Romain Billot
- UMR CNRS 6285 Lab-STICC, Institut Mines-Telecom, Brest, France
| | - Philippe Lenca
- IMT Atlantique, UMR CNRS 6285 Lab-STICC, Technopôle Brest Iroise, Brest, France
| | - David Delgado-Gomez
- Departamento de Estadistica, Universidad Carlos III de Madrid, Madrid, Spain
| | - Juliette Ropars
- LaTIM - INSERM UMR 1101, Brest Medical University Hospital, Brest, France
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Abstract
As molecular biology has increasingly become a data-intensive discipline, ontologies have emerged as an essential computational tool to assist in the organisation, description and analysis of data. Ontologies describe and classify the entities of interest in a scientific domain in a computationally accessible fashion such that algorithms and tools can be developed around them. The technology that underlies ontologies has its roots in logic-based artificial intelligence, allowing for sophisticated automated inference and error detection. This chapter presents a general introduction to modern computational ontologies as they are used in biology.
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Affiliation(s)
- Janna Hastings
- Cheminformatics and Metabolism, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
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Abstract
PURPOSE OF REVIEW Big data is the new hype in business and healthcare. Data storage and processing has become cheap, fast, and easy. Business analysts and scientists are trying to design methods to mine these data for hidden knowledge. Neurocritical care is a field that typically produces large amounts of patient-related data, and these data are increasingly being digitized and stored. This review will try to look beyond the hype, and focus on possible applications in neurointensive care amenable to Big Data research that can potentially improve patient care. RECENT FINDINGS The first challenge in Big Data research will be the development of large, multicenter, and high-quality databases. These databases could be used to further investigate recent findings from mathematical models, developed in smaller datasets. Randomized clinical trials and Big Data research are complementary. Big Data research might be used to identify subgroups of patients that could benefit most from a certain intervention, or can be an alternative in areas where randomized clinical trials are not possible. SUMMARY The processing and the analysis of the large amount of patient-related information stored in clinical databases is beyond normal human cognitive ability. Big Data research applications have the potential to discover new medical knowledge, and improve care in the neurointensive care unit.
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45
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Ltifi H, Benmohamed E, Kolski C, Ben Ayed M. Enhanced visual data mining process for dynamic decision-making. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2016.09.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Interactive knowledge discovery and data mining on genomic expression data with numeric formal concept analysis. BMC Bioinformatics 2016; 17:374. [PMID: 27628041 PMCID: PMC5024470 DOI: 10.1186/s12859-016-1234-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Accepted: 09/01/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Gene Expression Data (GED) analysis poses a great challenge to the scientific community that can be framed into the Knowledge Discovery in Databases (KDD) and Data Mining (DM) paradigm. Biclustering has emerged as the machine learning method of choice to solve this task, but its unsupervised nature makes result assessment problematic. This is often addressed by means of Gene Set Enrichment Analysis (GSEA). RESULTS We put forward a framework in which GED analysis is understood as an Exploratory Data Analysis (EDA) process where we provide support for continuous human interaction with data aiming at improving the step of hypothesis abduction and assessment. We focus on the adaptation to human cognition of data interpretation and visualization of the output of EDA. First, we give a proper theoretical background to bi-clustering using Lattice Theory and provide a set of analysis tools revolving around [Formula: see text]-Formal Concept Analysis ([Formula: see text]-FCA), a lattice-theoretic unsupervised learning technique for real-valued matrices. By using different kinds of cost structures to quantify expression we obtain different sequences of hierarchical bi-clusterings for gene under- and over-expression using thresholds. Consequently, we provide a method with interleaved analysis steps and visualization devices so that the sequences of lattices for a particular experiment summarize the researcher's vision of the data. This also allows us to define measures of persistence and robustness of biclusters to assess them. Second, the resulting biclusters are used to index external omics databases-for instance, Gene Ontology (GO)-thus offering a new way of accessing publicly available resources. This provides different flavors of gene set enrichment against which to assess the biclusters, by obtaining their p-values according to the terminology of those resources. We illustrate the exploration procedure on a real data example confirming results previously published. CONCLUSIONS The GED analysis problem gets transformed into the exploration of a sequence of lattices enabling the visualization of the hierarchical structure of the biclusters with a certain degree of granularity. The ability of FCA-based bi-clustering methods to index external databases such as GO allows us to obtain a quality measure of the biclusters, to observe the evolution of a gene throughout the different biclusters it appears in, to look for relevant biclusters-by observing their genes and what their persistence is-to infer, for instance, hypotheses on their function.
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Gamberger D, Ženko B, Mitelpunkt A, Shachar N, Lavrač N. Clusters of male and female Alzheimer's disease patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Brain Inform 2016; 3:169-179. [PMID: 27525218 PMCID: PMC4963443 DOI: 10.1007/s40708-016-0035-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Accepted: 01/21/2016] [Indexed: 10/26/2022] Open
Abstract
This paper presents homogeneous clusters of patients, identified in the Alzheimer's Disease Neuroimaging Initiative (ADNI) data population of 317 females and 342 males, described by a total of 243 biological and clinical descriptors. Clustering was performed with a novel methodology, which supports identification of patient subpopulations that are homogeneous regarding both clinical and biological descriptors. Properties of the constructed clusters clearly demonstrate the differences between female and male Alzheimer's disease patient groups. The major difference is the existence of two male subpopulations with unexpected values of intracerebral and whole brain volumes.
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Affiliation(s)
| | | | | | | | - Nada Lavrač
- Jožef Stefan Institute, Ljubljana, Slovenia
- University of Nova Gorica, Nova Gorica, Slovenia
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Pimentel A, Carreiro AV, Ribeiro RT, Gamboa H. Screening diabetes mellitus 2 based on electronic health records using temporal features. Health Informatics J 2016; 24:194-205. [PMID: 27566751 DOI: 10.1177/1460458216663023] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The prevalence of type 2 diabetes mellitus is increasing worldwide. Current methods of treating diabetes remain inadequate, and therefore, prevention with screening methods is the most appropriate process to reduce the burden of diabetes and its complications. We propose a new prognostic approach for type 2 diabetes mellitus based on electronic health records without using the current invasive techniques that are related to the disease (e.g. glucose level or glycated hemoglobin (HbA1c)). Our methodology is based on machine learning frameworks with data enrichment using temporal features. As as result our predictive model achieved an area under the receiver operating characteristics curve with a random forest classifier of 84.22 percent when including data information from 2009 to 2011 to predict diabetic patients in 2012, 83.19 percent when including temporal features, and 83.72 percent after applying temporal features and feature selection. We conclude that he pathology prediction is possible and efficient using the patient's progression information over the years and without using the invasive techniques that are currently used for type 2 diabetes mellitus classification.
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Jeanquartier F, Jean-Quartier C, Cemernek D, Holzinger A. In silico modeling for tumor growth visualization. BMC SYSTEMS BIOLOGY 2016; 10:59. [PMID: 27503052 PMCID: PMC4977902 DOI: 10.1186/s12918-016-0318-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 07/12/2016] [Indexed: 12/18/2022]
Abstract
BACKGROUND Cancer is a complex disease. Fundamental cellular based studies as well as modeling provides insight into cancer biology and strategies to treatment of the disease. In silico models complement in vivo models. Research on tumor growth involves a plethora of models each emphasizing isolated aspects of benign and malignant neoplasms. Biologists and clinical scientists are often overwhelmed by the mathematical background knowledge necessary to grasp and to apply a model to their own research. RESULTS We aim to provide a comprehensive and expandable simulation tool to visualizing tumor growth. This novel Web-based application offers the advantage of a user-friendly graphical interface with several manipulable input variables to correlate different aspects of tumor growth. By refining model parameters we highlight the significance of heterogeneous intercellular interactions on tumor progression. Within this paper we present the implementation of the Cellular Potts Model graphically presented through Cytoscape.js within a Web application. The tool is available under the MIT license at https://github.com/davcem/cpm-cytoscape and http://styx.cgv.tugraz.at:8080/cpm-cytoscape/ . CONCLUSION In-silico methods overcome the lack of wet experimental possibilities and as dry method succeed in terms of reduction, refinement and replacement of animal experimentation, also known as the 3R principles. Our visualization approach to simulation allows for more flexible usage and easy extension to facilitate understanding and gain novel insight. We believe that biomedical research in general and research on tumor growth in particular will benefit from the systems biology perspective.
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Affiliation(s)
- Fleur Jeanquartier
- Holzinger Group, Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, AT, Graz, Austria
| | - Claire Jean-Quartier
- Holzinger Group, Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, AT, Graz, Austria
| | - David Cemernek
- Holzinger Group, Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, AT, Graz, Austria
| | - Andreas Holzinger
- Holzinger Group, Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036, AT, Graz, Austria. .,Institute of Information Systems and Computer Media, Graz University of Technology, Inffeldgasse 16c, Graz, 8010, AT, Austria.
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Yu KH, Snyder M. Omics Profiling in Precision Oncology. Mol Cell Proteomics 2016; 15:2525-36. [PMID: 27099341 PMCID: PMC4974334 DOI: 10.1074/mcp.o116.059253] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 04/15/2016] [Indexed: 12/11/2022] Open
Abstract
Cancer causes significant morbidity and mortality worldwide, and is the area most targeted in precision medicine. Recent development of high-throughput methods enables detailed omics analysis of the molecular mechanisms underpinning tumor biology. These studies have identified clinically actionable mutations, gene and protein expression patterns associated with prognosis, and provided further insights into the molecular mechanisms indicative of cancer biology and new therapeutics strategies such as immunotherapy. In this review, we summarize the techniques used for tumor omics analysis, recapitulate the key findings in cancer omics studies, and point to areas requiring further research on precision oncology.
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Affiliation(s)
- Kun-Hsing Yu
- From the ‡Department of Genetics, Stanford University School of Medicine, Stanford, California; §Biomedical Informatics Program, Stanford University School of Medicine, Stanford, California
| | - Michael Snyder
- From the ‡Department of Genetics, Stanford University School of Medicine, Stanford, California;
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