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Biomarker Discovery in Personalized Medicine: Integrating Treatment Data, Multi-Omics Data, and Survival Outcomes

Presented During: Statistical analyses methods for integrating multi-omics data with health outcomes for biomarker discovery and personalized medication

Maiying Kong Co-Author
University of Louisville

Michael Sekula Co-Author

Maiying Kong Speaker
University of Louisville

Thursday, Aug 7: 8:55 AM - 9:15 AM
Topic-Contributed Paper Session

Music City Center

Personalized medicine has become a cornerstone of modern healthcare, as one treatment does not fit all patients. With advancements in technology, omics data and high-dimensional datasets have become increasingly available, providing new opportunities for discovering biomarkers in personalized medicine. Integrating treatment information with multi-omics data can help identify signature molecules that serve as both prognostic and predictive variables. In this talk, we will explore how incorporating various multivariate analysis techniques—such as factor analysis, principal component analysis, and regularized variable selection methods—can effectively identify prognostic biomarkers and treatment effect modifiers.

Keywords

Biomarker discovery

Survival outcomes

Effect modifiers

prognostic biomarkers