Cardiovascular informatics: building a bridge to data harmony.

Journal: Cardiovascular research
Published Date: Feb 21, 2022

Abstract

The search for new strategies for better understanding cardiovascular (CV) disease is a constant one, spanning multitudinous types of observations and studies. A comprehensive characterization of each disease state and its biomolecular underpinnings relies upon insights gleaned from extensive information collection of various types of data. Researchers and clinicians in CV biomedicine repeatedly face questions regarding which types of data may best answer their questions, how to integrate information from multiple datasets of various types, and how to adapt emerging advances in machine learning and/or artificial intelligence to their needs in data processing. Frequently lauded as a field with great practical and translational potential, the interface between biomedical informatics and CV medicine is challenged with staggeringly massive datasets. Successful application of computational approaches to decode these complex and gigantic amounts of information becomes an essential step toward realizing the desired benefits. In this review, we examine recent efforts to adapt informatics strategies to CV biomedical research: automated information extraction and unification of multifaceted -omics data. We discuss how and why this interdisciplinary space of CV Informatics is particularly relevant to and supportive of current experimental and clinical research. We describe in detail how open data sources and methods can drive discovery while demanding few initial resources, an advantage afforded by widespread availability of cloud computing-driven platforms. Subsequently, we provide examples of how interoperable computational systems facilitate exploration of data from multiple sources, including both consistently formatted structured data and unstructured data. Taken together, these approaches for achieving data harmony enable molecular phenotyping of CV diseases and unification of CV knowledge.

Authors

  • John Harry Caufield
    NHLBI Integrated Cardiovascular Data Science Training Program at University of California, Los Angeles (UCLA), Suite 1-609, MRL Building, 675 Charles E. Young Dr. South, Los Angeles, CA 90095-1760, USA.
  • Dibakar Sigdel
    NIH BD2K Program Centers of Excellence for Big Data Computing-Heart BD2K Center, Departments of Physiology, Medicine/Cardiology, and Bioinformatics, David Geffen School of Medicine, University of California , Los Angeles, California.
  • John Fu
    NHLBI Integrated Cardiovascular Data Science Training Program at University of California, Los Angeles (UCLA), Suite 1-609, MRL Building, 675 Charles E. Young Dr. South, Los Angeles, CA 90095-1760, USA.
  • Howard Choi
    NIH BD2K Program Centers of Excellence for Big Data Computing-Heart BD2K Center, Departments of Physiology, Medicine/Cardiology, and Bioinformatics, David Geffen School of Medicine, University of California , Los Angeles, California.
  • Vladimir Guevara-Gonzalez
    NHLBI Integrated Cardiovascular Data Science Training Program at University of California, Los Angeles (UCLA), Suite 1-609, MRL Building, 675 Charles E. Young Dr. South, Los Angeles, CA 90095-1760, USA.
  • Ding Wang
  • Peipei Ping
    From the NIH BD2K Center of Excellence for Biomedical Computing at UCLA, Los Angeles, CA (P.P., K.W., A.B.); and NIH BD2K KnowEng Center of Excellence for Biomedical Computing at UIUC, Urbana, IL (J.H.). pping38@g.ucla.edu.

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