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Machine Learning and Biomarker Sub-Group Analysis for Precision Medicine

Application Area:
Life Sciences

Routinely including genomic and other biological data in clinical research, practice and care is increasingly transforming decision making processes in precision medicine and prediction health. For example, the FDA reports that pharmacogenomic biomarkers are now included in drug labeling of over 400 drugs. Learn how modern machine learning and statistical techniques can be applied for prognostic (e.g. to determine if a patient should receive treatment) and predictive (e.g. to determine patient outcome or risk to given treatment) analytic approaches to biomarker discovery for precision medicine and health.  See how to use JMP Genomics machine learning and subgroup exploration capabilities to model the traditionally wide genomic data problems that contain many thousands of DNA, gene, metabolite or protein signatures.

This webinar covers: Predictive Model Review Builder; and prognostic sub-group analysis including  Interaction Trees, Virtual Twins and Optimal Treatment Regimes.  

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