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Personal Genomic Measurements: The Opportunity for Information Integration
Author(s) -
Altman R B
Publication year - 2013
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1038/clpt.2012.203
Subject(s) - bayes' theorem , personalized medicine , a priori and a posteriori , imperfect , medical diagnosis , data integration , population , clinical pharmacology , computer science , data science , precision medicine , computational biology , data mining , medicine , bioinformatics , biology , artificial intelligence , bayesian probability , pathology , philosophy , linguistics , environmental health , epistemology
High‐throughput genomic measurements initially emerged for research purposes but are now entering the clinic. The challenge for clinicians is to integrate imperfect genomic measurements with other information sources so as to estimate as closely as possible the probabilities of clinical events (diagnoses, treatment responses, prognoses). Population‐based data provide a priori probabilities that can be combined with individual measurements to compute a posteriori estimates using Bayes' rule. Thus, the integration of population science with individual genomic measurements will enable the practice of personalized medicine. Clinical Pharmacology & Therapeutics (2013); 93 1, 21–23. doi: 10.1038/clpt.2012.203