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.
| Identifer | oai:union.ndltd.org:harvard.edu/oai:dash.harvard.edu:1/11169763 |
| Date | 14 October 2013 |
| Creators | Huang, Norman Jason |
| Contributors | Li, Chenggang |
| Publisher | Harvard University |
| Source Sets | Harvard University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Rights | open |
Page generated in 0.0022 seconds