Graph-based Support Vector Machines for Patient Response Prediction Using Pathway and Gene Expression Data
Contributors: Li, Cheng; Cai, Tianxi; Munshi, Nikhil
Published: 2013
Language: eng
Description: Over the past decade, multiple function genomic datasets studying chromosomal aberrations and their downstream implications on gene expression have accumulated across a variety of cancer types. With the majority being paired copy number/gene expression profiles originating from the same patient groups, this time frame has also induced a wealth of integrative attempts in hope that the concurrent analysis between both genomic structures will result in optimized downstream results. Borrowing the concept, this dissertation presents a novel contribution to the development of statistical methodology for integrating copy number and gene expression data for purposes of predicting treatment response in multiple myeloma patients.
Notes: Keywords: Expression Data, Integration, Pathway Information, Response Prediction, SVM.

Thesis Ph.D. Harvard University 2013