Identifying Gene Signatures from Cancer Progression Data Using Ordinal Analysis

—A comprehensive understanding of cancer progression may shed light on genetic and molecular mechanisms of oncogenesis, and it may provide much needed information for effective diagnosis, prognosis, and optimal therapy. However, despite considerable effort in studying cancer progressions, their molecular and genetic basis remains largely unknown. Microarray experiments can systematically assay gene expressions across genome, therefore they have been widely used to gain insights on cancer progressions. In general, expression data may be obtained from different stages of the same samples. More often, data were obtained from individuals at different stages. Existing methods such as the Student’s t-test and clustering approaches focus on identification of differentially expressed genes in different stages, but they are not suitable for capturing real progression signatures across all progression stages. We propose an alternative approach, namely a multicategory logit model, to identify...

• Type: Conference
• Year: 2009
• Where: BIBM
• Authors: Yoon Soo Pyon, Jing Li
• Keywords: BIBM 2009, Bioinformatics, Cancer Progression, Multiple Stages, Prostate Cancer Progression