title: ICP: A novel approach to predict prognosis of prostate cancer with inner-class clustering of gene expression data.
abstract: Prostate cancer has heterogeneous characteristics. For that reason, even if tumors appear histologically similar to each other, there are many cases in which they are actually different, based on their gene expression levels. A single tumor may have multiple expression levels with both high-risk cancer genes and low-risk cancer genes. We can produce more useful models for stratifying prostate cancers into high-risk cancer and low-risk cancer categories by considering the range in each class through inner-class clustering. In this paper, we attempt to classify cancers into high-risk (aggressive) prostate cancer and low-risk (non-aggressive) prostate cancer using ICP (Inner-class Clustering and Prediction). Our model classified more efficiently than the models of the algorithms used for comparison. After discovering a number of genes linked to prostate cancer from the gene pairs used in our classification, we discovered that the proposed method can be used to find new unknown genes and gene pairs which distinguish between high-risk cancer and low-risk cancer.
authors.affiliation: Department of Computer Science, Yonsei University, South Korea.
authors.forename: Hyunjin
authors.initials: H
authors.lastname: Kim
authors.forename: Jaegyoon
authors.initials: J
authors.lastname: Ahn
authors.forename: Chihyun
authors.initials: C
authors.lastname: Park
authors.forename: Youngmi
authors.initials: Y
authors.lastname: Yoon
authors.forename: Sanghyun
authors.initials: S
authors.lastname: Park
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis / Q000235:genetics / Q000378:metabolism; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008297:Male; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000235:genetics* / Q000378:metabolism
chemical_list: D014408:Biomarkers, Tumor
keywords: Bioinformatics; Classification; Clustering; Microarray data analysis; Prognosis; Prostate cancer