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)