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

DSpace/Manakin Repository

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

Citable link to this page

 

 
Title: Graph-based Support Vector Machines for Patient Response Prediction Using Pathway and Gene Expression Data
Author: Huang, Norman Jason
Citation: Huang, Norman Jason. 2013. Graph-based Support Vector Machines for Patient Response Prediction Using Pathway and Gene Expression Data. Doctoral dissertation, Harvard University.
Full Text & Related Files:
Abstract: 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.
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11169763
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters