title: A novel class dependent feature selection method for cancer biomarker discovery.
abstract: Identifying key biomarkers for different cancer types can improve diagnosis accuracy and treatment. Gene expression data can help differentiate between cancer subtypes. However the limitation of having a small number of samples versus a larger number of genes represented in a dataset leads to the overfitting of classification models. Feature selection methods can help select the most distinguishing feature sets for classifying different cancers. A new class dependent feature selection approach integrates the F-statistic, Maximum Relevance Binary Particle Swarm Optimization (MRBPSO) and Class Dependent Multi-category Classification (CDMC) system. This feature selection method combines filter and wrapper based methods. A set of highly differentially expressed genes (features) are pre-selected using the F statistic for each dataset as a filter for selecting the most meaningful features. MRBPSO and CDMC function as a wrapper to select desirable feature subsets for each class and classify the samples using those chosen class-dependent feature subsets. The performance of the proposed methods is evaluated on eight real cancer datasets. The results indicate that the class-dependent approaches can effectively identify biomarkers related to each cancer type and improve classification accuracy compared to class independent feature selection methods.
authors.affiliation: DuPont Pioneer, 7200 NW 62nd Avenue, Johnston, IA 50131, USA. Electronic address: wengang.zhou@pioneer.com.
authors.forename: Wengang
authors.initials: W
authors.lastname: Zhou
authors.affiliation: Electrical and Computer Engineering Department, Iowa State University, Ames, IA 50010, USA. Electronic address: julied@iastate.edu.
authors.forename: Julie A
authors.initials: JA
authors.lastname: Dickerson
mesh_terms: D014408:Biomarkers, Tumor / Q000145:classification* / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D017143:Fuzzy Logic; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism*; D060388:Support Vector Machine
chemical_list: D014408:Biomarkers, Tumor
keywords: Binary particle swarm optimization; Cancer biomarker discovery; Class dependent multi-category classification; Feature selection; Support vector machine