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Holt KD, Roman G, McIntosh L, Kleinsorge J, Holden-Wiltse J, Bennett NM. RocHealthData.org: Development and usage of a publicly available, geographic source of social determinants of health data. J Clin Transl Sci 2024; 8:e41. [PMID: 38476248 PMCID: PMC10928699 DOI: 10.1017/cts.2024.484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 12/27/2023] [Accepted: 02/02/2024] [Indexed: 03/14/2024] Open
Abstract
Access to local, population specific, and timely data is vital in understanding factors that impact population health. The impact of place (neighborhood, census tract, and city) is particularly important in understanding the Social Determinants of Health. The University of Rochester Medical Center's Clinical and Translational Science Institute created the web-based tool RocHealthData.org to provide access to thousands of geographically displayed publicly available health-related datasets. The site has also hosted a variety of locally curated datasets (eg., COVID-19 vaccination rates and community-derived health indicators), helping set community priorities and impacting outcomes. Usage statistics (available through Google Analytics) show returning visitors with a lower bounce rate (leaving a site after a single page access) and spent longer at the site than new visitors. Of the currently registered 1033 users, 51.7% were from within our host university, 20.1% were from another educational institution, and 28.2% identified as community members. Our assessments indicate that these data are useful and valued across a variety of domains. Continuing site improvement depends on new sources of locally relevant data, as well as increased usage of data beyond our local region.
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Affiliation(s)
- Kathleen D. Holt
- Clinical and Translational Science Institute, University of Rochester, Rochester, NY, USA
- Center for Community Health and Prevention, University of Rochester, Rochester, NY, USA
| | - Gretchen Roman
- Department of Family Medicine, University of Rochester, Rochester, NY, USA
| | - Laura McIntosh
- Center for Community Health and Prevention, University of Rochester, Rochester, NY, USA
| | - Jamie Kleinsorge
- Center for Applied Research and Engagement Systems, University of Missouri, Columbia, MO, USA
| | - Jeanne Holden-Wiltse
- Clinical and Translational Science Institute, University of Rochester, Rochester, NY, USA
| | - Nancy M. Bennett
- Clinical and Translational Science Institute, University of Rochester, Rochester, NY, USA
- Department of Medicine, School of Medicine and Dentistry, Rochester, NY, USA
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2
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Welter D, Juty N, Rocca-Serra P, Xu F, Henderson D, Gu W, Strubel J, Giessmann RT, Emam I, Gadiya Y, Abbassi-Daloii T, Alharbi E, Gray AJG, Courtot M, Gribbon P, Ioannidis V, Reilly DS, Lynch N, Boiten JW, Satagopam V, Goble C, Sansone SA, Burdett T. FAIR in action - a flexible framework to guide FAIRification. Sci Data 2023; 10:291. [PMID: 37208349 PMCID: PMC10199076 DOI: 10.1038/s41597-023-02167-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 03/28/2023] [Indexed: 05/21/2023] Open
Abstract
The COVID-19 pandemic has highlighted the need for FAIR (Findable, Accessible, Interoperable, and Reusable) data more than any other scientific challenge to date. We developed a flexible, multi-level, domain-agnostic FAIRification framework, providing practical guidance to improve the FAIRness for both existing and future clinical and molecular datasets. We validated the framework in collaboration with several major public-private partnership projects, demonstrating and delivering improvements across all aspects of FAIR and across a variety of datasets and their contexts. We therefore managed to establish the reproducibility and far-reaching applicability of our approach to FAIRification tasks.
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Affiliation(s)
- Danielle Welter
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg
| | - Nick Juty
- University of Manchester, Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX13QG, Oxford, UK
| | - Fuqi Xu
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - David Henderson
- Bayer AG, Business Development & Licensing & OI, Muellerstrasse 178, 13353, Berlin, Germany
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg
| | - Jolanda Strubel
- The Hyve BV, Arthur van Schendelstraat 650, 3511 MJ, Utrecht, The Netherlands
| | - Robert T Giessmann
- Bayer AG, Business Development & Licensing & OI, Muellerstrasse 178, 13353, Berlin, Germany
- Institute for Globally Distributed Open Research and Education (IGDORE), Gothenburg, Sweden
| | - Ibrahim Emam
- Data Science Institute, Imperial College, London, UK
| | - Yojana Gadiya
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP) and Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590, Frankfurt, Germany
| | - Tooba Abbassi-Daloii
- Department of Bioinformatics (BiGCaT), NUTRIM, FHML, Maastricht University, Maastricht, The Netherlands
| | - Ebtisam Alharbi
- College of Computer and Information Systems, Umm Al-Qura University, Mecca, Saudi Arabia
| | - Alasdair J G Gray
- Department of Computer Science, Heriot-Watt University, Edinburgh, EH14 4AS, Scotland, UK
| | - Melanie Courtot
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
- Ontario Institute for Cancer Research MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario, M5G 0A3, Canada
| | - Philip Gribbon
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP) and Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590, Frankfurt, Germany
| | - Vassilios Ioannidis
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Dorothy S Reilly
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland
| | | | | | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg
| | - Carole Goble
- University of Manchester, Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX13QG, Oxford, UK
| | - Tony Burdett
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK.
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Rocca-Serra P, Gu W, Ioannidis V, Abbassi-Daloii T, Capella-Gutierrez S, Chandramouliswaran I, Splendiani A, Burdett T, Giessmann RT, Henderson D, Batista D, Emam I, Gadiya Y, Giovanni L, Willighagen E, Evelo C, Gray AJG, Gribbon P, Juty N, Welter D, Quast K, Peeters P, Plasterer T, Wood C, van der Horst E, Reilly D, van Vlijmen H, Scollen S, Lister A, Thurston M, Granell R, Sansone SA. The FAIR Cookbook - the essential resource for and by FAIR doers. Sci Data 2023; 10:292. [PMID: 37208467 PMCID: PMC10198982 DOI: 10.1038/s41597-023-02166-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 04/19/2023] [Indexed: 05/21/2023] Open
Abstract
The notion that data should be Findable, Accessible, Interoperable and Reusable, according to the FAIR Principles, has become a global norm for good data stewardship and a prerequisite for reproducibility. Nowadays, FAIR guides data policy actions and professional practices in the public and private sectors. Despite such global endorsements, however, the FAIR Principles are aspirational, remaining elusive at best, and intimidating at worst. To address the lack of practical guidance, and help with capability gaps, we developed the FAIR Cookbook, an open, online resource of hands-on recipes for "FAIR doers" in the Life Sciences. Created by researchers and data managers professionals in academia, (bio)pharmaceutical companies and information service industries, the FAIR Cookbook covers the key steps in a FAIRification journey, the levels and indicators of FAIRness, the maturity model, the technologies, the tools and the standards available, as well as the skills required, and the challenges to achieve and improve data FAIRness. Part of the ELIXIR ecosystem, and recommended by funders, the FAIR Cookbook is open to contributions of new recipes.
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Affiliation(s)
- Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX13QG, Oxford, UK.
- AstraZeneca, Data Office, Data Science & AI unit R&D, 136 Hills Rd, Cambridge, UK.
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg
- Luxembourg National Data Service, 6 Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg, L-4362, Esch-sur-Alzette, Luxembourg
| | - Vassilios Ioannidis
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Tooba Abbassi-Daloii
- Department of Bioinformatics (BiGCaT), NUTRIM, FHML, Maastricht University, Maastricht, the Netherlands
| | | | - Ishwar Chandramouliswaran
- Office of Data Science Strategy, National Institutes of Health, 9000 Rockville Pike, Bethesda, Maryland, 20892, USA
| | | | - Tony Burdett
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
| | - Robert T Giessmann
- Bayer AG, Business Development & Licensing & OI, Pharmaceuticals, 13342, Berlin, Germany
- Institute for Globally Distributed Open Research and Education (IGDORE), Berlin, Germany
| | - David Henderson
- Bayer AG, Business Development & Licensing & OI, Pharmaceuticals, 13342, Berlin, Germany
| | - Dominique Batista
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX13QG, Oxford, UK
| | - Ibrahim Emam
- Data Science Institute, Imperial College London, William Penney Laboratory, South Kensington Campus, London, SW7 2AZ, UK
| | - Yojana Gadiya
- Fraunhofer Institute for Translational Medicine and Pharmacology and Fraunhofer Cluster of Excellence for Immune Mediated Diseases, Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590, Frankfurt, Germany
| | - Lucas Giovanni
- Department of Bioinformatics (BiGCaT), NUTRIM, FHML, Maastricht University, Maastricht, the Netherlands
| | - Egon Willighagen
- Department of Bioinformatics (BiGCaT), NUTRIM, FHML, Maastricht University, Maastricht, the Netherlands
| | - Chris Evelo
- Department of Bioinformatics (BiGCaT), NUTRIM, FHML, Maastricht University, Maastricht, the Netherlands
| | - Alasdair J G Gray
- Department of Computer Science, Heriot-Watt University, Edinburgh, EH14 4AS, Scotland, UK
| | - Philip Gribbon
- Fraunhofer Institute for Translational Medicine and Pharmacology and Fraunhofer Cluster of Excellence for Immune Mediated Diseases, Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590, Frankfurt, Germany
| | - Nick Juty
- The University of Manchester, Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Danielle Welter
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367, Belval, Luxembourg
- Luxembourg National Data Service, 6 Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg, L-4362, Esch-sur-Alzette, Luxembourg
| | - Karsten Quast
- Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397, Biberach an der Riss, Germany
| | - Paul Peeters
- Janssen, Turnhoutseweg 30, B-2340, Beerse, Belgium
| | - Tom Plasterer
- AstraZeneca Pharmaceuticals, 36 Gatehouse Drive, Waltham, MA, 02451, USA
| | - Colin Wood
- AstraZeneca, da Vinci Building, Melbourn Science Park, Cambridge Road, Royston, SG8 6HM, UK
| | - Eelke van der Horst
- The Hyve BV, Arthur van Schendelstraat 650, 3511 MJ, Utrecht, The Netherlands
| | - Dorothy Reilly
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland
| | | | - Serena Scollen
- ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Allyson Lister
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX13QG, Oxford, UK
| | - Milo Thurston
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX13QG, Oxford, UK
| | - Ramon Granell
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX13QG, Oxford, UK
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX13QG, Oxford, UK.
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Holmgren S, Bell SM, Wignall J, Duncan CG, Kwok RK, Cronk R, Osborn K, Black S, Thessen A, Schmitt C. Workshop Report: Catalyzing Knowledge-Driven Discovery in Environmental Health Sciences through a Harmonized Language. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2317. [PMID: 36767684 PMCID: PMC9915042 DOI: 10.3390/ijerph20032317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/20/2023] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
Harmonized language is essential to finding, sharing, and reusing large-scale, complex data. Gaps and barriers prevent the adoption of harmonized language approaches in environmental health sciences (EHS). To address this, the National Institute of Environmental Health Sciences and partners created the Environmental Health Language Collaborative (EHLC). The purpose of EHLC is to facilitate a community-driven effort to advance the development and adoption of harmonized language approaches in EHS. EHLC is a forum to pinpoint language harmonization gaps, to facilitate the development of, raise awareness of, and encourage the use of harmonization approaches and tools, and to develop new standards and recommendations. To ensure that EHLC's focus and structure would be sustainable long-term and meet the needs of the field, EHLC launched an inaugural workshop in September 2021 focused on "Developing Sustainable Language Solutions" and "Building a Sustainable Community". When the attendees were surveyed, 91% said harmonized language solutions would be of high value/benefit, and 60% agreed to continue contributing to EHLC efforts. Based on workshop discussions, future activities will focus on targeted collaborative use-case working groups in addition to offering education and training on ontologies, metadata, and standards, and developing an EHS language resource portal.
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Affiliation(s)
- Stephanie Holmgren
- Office of Data Science, National Institute of Environmental Health Sciences (NIEHS), Durham, NC 27709, USA
| | | | | | - Christopher G. Duncan
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences (NIEHS), Durham, NC 27709, USA
| | - Richard K. Kwok
- Division of Neuroscience, National Institute on Aging (NIA), Bethesda, MD 20892, USA
| | - Ryan Cronk
- Health Sciences, ICF, Reston, VA 20190, USA
| | | | | | - Anne Thessen
- Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Charles Schmitt
- Office of Data Science, National Institute of Environmental Health Sciences (NIEHS), Durham, NC 27709, USA
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Alharbi E, Gadiya Y, Henderson D, Zaliani A, Delfin-Rossaro A, Cambon-Thomsen A, Kohler M, Witt G, Welter D, Juty N, Jay C, Engkvist O, Goble C, Reilly DS, Satagopam V, Ioannidis V, Gu W, Gribbon P. Selection of data sets for FAIRification in drug discovery and development: Which, why, and how? Drug Discov Today 2022; 27:2080-2085. [PMID: 35595012 PMCID: PMC9236643 DOI: 10.1016/j.drudis.2022.05.010] [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: 01/14/2022] [Revised: 04/28/2022] [Accepted: 05/10/2022] [Indexed: 11/30/2022]
Abstract
Research organisations are focussed on quantifying the costs and benefits of implementing FAIR. Criteria used for the selection of data for FAIRification can be opaque and inconsistent. FAIRification effort depends on individual skills, competencies, resources, and time available. FAIRification should satisfy reuse scenarios, and lead to scientific and economic impacts. Organisational challenges include providing training to individuals and developing a FAIR organisation culture.
Despite the intuitive value of adopting the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in both academic and industrial sectors, challenges exist in resourcing, balancing long- versus short-term priorities, and achieving technical implementation. This situation is exacerbated by the unclear mechanisms by which costs and benefits can be assessed when decisions on FAIR are made. Scientific and research and development (R&D) leadership need reliable evidence of the potential benefits and information on effective implementation mechanisms and remediating strategies. In this article, we describe procedures for cost–benefit evaluation, and identify best-practice approaches to support the decision-making process involved in FAIR implementation.
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Affiliation(s)
- Ebtisam Alharbi
- Department of Computer Science, The University of Manchester, Oxford Road, Manchester, UK
| | - Yojana Gadiya
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590 Frankfurt, Germany; Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590 Frankfurt, Germany
| | - David Henderson
- Bayer AG, Research & Development, Pharmaceuticals, Müllerstrasse 178, 13353 Berlin, Germany
| | - Andrea Zaliani
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590 Frankfurt, Germany; Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590 Frankfurt, Germany
| | | | | | - Manfred Kohler
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590 Frankfurt, Germany; Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590 Frankfurt, Germany
| | - Gesa Witt
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590 Frankfurt, Germany; Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590 Frankfurt, Germany
| | - Danielle Welter
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367 Belval, Luxembourg
| | - Nick Juty
- Department of Computer Science, The University of Manchester, Oxford Road, Manchester, UK
| | - Caroline Jay
- Department of Computer Science, The University of Manchester, Oxford Road, Manchester, UK
| | - Ola Engkvist
- Discovery Sciences, R&D, AstraZeneca, SE-43183 Mölndal, Sweden
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Oxford Road, Manchester, UK
| | - Dorothy S Reilly
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, ELIXIR Luxembourg, University of Luxembourg, L-4367 Belval, Luxembourg
| | - Vassilios Ioannidis
- SIB Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Amphipole, 1015 Lausanne, Switzerland.
| | - Wei Gu
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland.
| | - Philip Gribbon
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525 Hamburg, and Theodor Stern Kai 7, 60590 Frankfurt, Germany; Fraunhofer Cluster of Excellence for Immune Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590 Frankfurt, Germany.
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Martínez-García M, Hernández-Lemus E. Data Integration Challenges for Machine Learning in Precision Medicine. Front Med (Lausanne) 2022; 8:784455. [PMID: 35145977 PMCID: PMC8821900 DOI: 10.3389/fmed.2021.784455] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/28/2021] [Indexed: 12/19/2022] Open
Abstract
A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.
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Affiliation(s)
- Mireya Martínez-García
- Clinical Research Division, National Institute of Cardiology ‘Ignacio Chávez’, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autnoma de Mexico, Mexico City, Mexico
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