An unsupervised learning approach to find ovarian cancer genes through integration of biological data

BMC Genomics
Christopher MaJinghui Zhang

Abstract

Cancer is a disease characterized largely by the accumulation of out-of-control somatic mutations during the lifetime of a patient. Distinguishing driver mutations from passenger mutations has posed a challenge in modern cancer research. With the advanced development of microarray experiments and clinical studies, a large numbers of candidate cancer genes have been extracted and distinguishing informative genes out of them is essential. As a matter of fact, we proposed to find the informative genes for cancer by using mutation data from ovarian cancers in our framework. In our model we utilized the patient gene mutation profile, gene expression data and gene gene interactions network to construct a graphical representation of genes and patients. Markov processes for mutation and patients are triggered separately. After this process, cancer genes are prioritized automatically by examining their scores at their stationary distributions in the eigenvector. Extensive experiments demonstrate that the integration of heterogeneous sources of information is essential in finding important cancer genes.

References

Jul 27, 1999·Gynecologic Oncology·R L BaldwinB Y Karlan
Jan 7, 2000·Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology·H J MackayR Brown
Apr 6, 2001·The American Journal of Pathology·I Meinhold-HeerleinJ C Reed
Sep 27, 2003·Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology·Elise RenkonenPaivi Peltomaki
Aug 3, 2004·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Minghua DengFengzhu Sun
May 28, 2005·Bioinformatics·Jason McDermottRam Samudrala
Sep 15, 2005·International Journal of Cancer. Journal International Du Cancer·Michael ScottS E Hilary Russell
Dec 13, 2005·Molecular and Cellular Biochemistry·Jean-Philippe GagnéGuy G Poirier
Mar 15, 2006·Clinical Cancer Research : an Official Journal of the American Association for Cancer Research·Nicola TinariStefano Iacobelli
May 9, 2006·Nature Biotechnology·Stein AertsYves Moreau
Sep 28, 2006·Breast Cancer Research and Treatment·Emma-Leena AlarmoAnne Kallioniemi
Oct 13, 2006·Neoplasia : an International Journal for Oncology Research·Sara VignatiCarlo V Catapano
Mar 14, 2007·Molecular Systems Biology·Roded SharanRon Shamir
May 9, 2008·PLoS Biology·Kerstin B MeyerBruce A J Ponder
Oct 31, 2009·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Jie Chen, Yu-Ping Wang
Nov 1, 2011·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Sinan ErtenMehmet Koyutürk
Oct 26, 2012·BMC Bioinformatics·Matteo Re, Giorgio Valentini
Jan 18, 2013·Cell Cycle·Karen McLean, Ronald J Buckanovich
May 3, 2013·Nature·Cyriac KandothDouglas A Levine
Oct 17, 2013·BioData Mining·Deanna PetrochilosNeil Abernethy

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