Cancer progression analysis based on ordinal relationship of cancer stages and co-expression network modularity
by Yoon Soo Pyon, Xin Li, Jing Li
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 5, No. 3, 2011

Abstract: A comprehensive understanding of cancer progression may shed light on genetic and molecular mechanisms of oncogenesis, and provide important information for effective diagnosis and prognosis. We propose a multicategory logit model to identify genes that show significant correlations across multiple cancer stages. We have applied the approach on a Prostate Cancer (PCA) progression data and obtained a set of genes that show consistent trends across multiple stages. Further analysis based on multiple evidences demonstrates that our candidate list includes not only some well-known prostate-cancer-related genes, but also novel genes that have been confirmed very recently.

Online publication date: Sat, 24-Jan-2015

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