Graph-based Support Vector Machines for Patient Response Prediction Using Pathway and Gene Expression Data

Li, ChenggangHuang, Norman Jason2013-10-142013-10-142013Huang, Norman Jason. 2013. Graph-based Support Vector Machines for Patient Response Prediction Using Pathway and Gene Expression Data. Doctoral dissertation, Harvard University.http://dissertations.umi.com/gsas.harvard:11072http://nrs.harvard.edu/urn-3:HUL.InstRepos:11169763Over 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.en-USBiostatisticsExpression DataIntegrationPathway InformationResponse PredictionSVMGraph-based Support Vector Machines for Patient Response Prediction Using Pathway and Gene Expression DataThesis or Dissertation2013-10-14