Precision Medicine: Applying Machine Learning to Omics Data for improved treatment selection

Introduction: theoretical demonstration/lecture

– overview of challenges in precision medicine from a data perspective, especially precise classification of cancers (i.e. breast cancer subtypes) and the utility of such approaches for treatment selection

– hypothesis driven vs. data driven approach to high-throughput biomedical data

– utilization of cell-lines derived from patients for high throughput screening

Omics data and Machine Learning (hands-on portion)

– transcriptomics: mRNA expression patterns – how to get meaningful data from sequencing

– conventional machine learning tools for unsupervised and supervised analysis

– integration (integrating IG50 profiles from drug screening with RNA-seq data from patients)