title: iPcc: a novel feature extraction method for accurate disease class discovery and prediction.
abstract: Gene expression profiling has gradually become a routine procedure for disease diagnosis and classification. In the past decade, many computational methods have been proposed, resulting in great improvements on various levels, including feature selection and algorithms for classification and clustering. In this study, we present iPcc, a novel method from the feature extraction perspective to further propel gene expression profiling technologies from bench to bedside. We define 'correlation feature space' for samples based on the gene expression profiles by iterative employment of Pearson's correlation coefficient. Numerical experiments on both simulated and real gene expression data sets demonstrate that iPcc can greatly highlight the latent patterns underlying noisy gene expression data and thus greatly improve the robustness and accuracy of the algorithms currently available for disease diagnosis and classification based on gene expression profiles.
authors.affiliation: MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
authors.forename: Xianwen
authors.initials: X
authors.lastname: Ren
authors.forename: Yong
authors.initials: Y
authors.lastname: Wang
authors.forename: Xiang-Sun
authors.initials: XS
authors.lastname: Zhang
authors.forename: Qi
authors.initials: Q
authors.lastname: Jin
mesh_terms: D000465:Algorithms; D002965:Classification / Q000379:methods; D016000:Cluster Analysis; D019937:Diagnostic Techniques and Procedures; D004194:Disease / Q000145:classification* / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D007938:Leukemia / Q000145:classification / Q000235:genetics; D008297:Male; D011471:Prostatic Neoplasms / Q000145:classification / Q000235:genetics; D011565:Psoriasis / Q000145:classification / Q000235:genetics