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Data Heterogeneity: The Enzyme to Catalyze Translational Bioinformatics?
Author(s) -
Eli M. Cahan,
Purvesh Khatri
Publication year - 2020
Publication title -
jmir. journal of medical internet research/journal of medical internet research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.446
H-Index - 142
eISSN - 1439-4456
pISSN - 1438-8871
DOI - 10.2196/18044
Subject(s) - precision medicine , psychological intervention , personalized medicine , health care , bench to bedside , data science , diversity (politics) , translational research , medline , computer science , big data , medicine , computational biology , bioinformatics , data mining , biology , medical physics , nursing , pathology , biochemistry , sociology , anthropology , economics , economic growth
Up to 95% of novel interventions demonstrating significant effects at the bench fail to translate to the bedside. In recent years, the windfalls of “big data” have afforded investigators more substrate for research than ever before. However, issues with translation have persisted: although countless biomarkers for diagnostic and therapeutic targeting have been proposed, few of these generalize effectively. We assert that inadequate heterogeneity in datasets used for discovery and validation causes their nonrepresentativeness of the diversity observed in real-world patient populations. This nonrepresentativeness is contrasted with advantages rendered by the solicitation and utilization of data heterogeneity for multisystemic disease modeling. Accordingly, we propose the potential benefits of models premised on heterogeneity to promote the Institute for Healthcare Improvement’s Triple Aim. In an era of personalized medicine, these models can confer higher quality clinical care for individuals, increased access to effective care across all populations, and lower costs for the health care system.

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