title: Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles.
abstract: Background. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous challenges for its characteristics, such as high dimensionality, small sample size, and low Signal-to-Noise Ratio.Results. This paper proposes a method, termed RS_SVM, to predict gene expression profiles via aggregating SVM trained on random subspaces. After choosing gene features through statistical analysis, RS_SVM randomly selects feature subsets to yield random subspaces and training SVM classifiers accordingly and then aggregates SVM classifiers to capture the advantage of ensemble learning. Experiments on eight real gene expression datasets are performed to validate the RS_SVM method. Experimental results show that RS_SVM achieved better classification accuracy and generalization performance in contrast with single SVM,K-nearest neighbor, decision tree, Bagging, AdaBoost, and the state-of-the-art methods. Experiments also explored the effect of subspace size on prediction performance.Conclusions. The proposed RS_SVM method yielded superior performance in analyzing gene expression profiles, which demonstrates that RS_SVM provides a good channel for such biological data.
authors.affiliation: School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China.
authors.forename: Liying
authors.identifier: 0000-0002-4336-9014
authors.initials: L
authors.lastname: Yang
authors.affiliation: School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China.
authors.forename: Zhimin
authors.identifier: 0000-0001-5943-7643
authors.initials: Z
authors.lastname: Liu
authors.affiliation: School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China.
authors.forename: Xiguo
authors.initials: X
authors.lastname: Yuan
authors.affiliation: State Key Laboratory of Military Stomatology, Department of Maxillofacial Surgery, School of Stomatology, the Fourth Military Medical University, Xi'an, China.
authors.forename: Jianhua
authors.identifier: 0000-0002-8231-3101
authors.initials: J
authors.lastname: Wei
authors.affiliation: School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China.
authors.forename: Junying
authors.initials: J
authors.lastname: Zhang
mesh_terms: D000818:Animals; D030561:Databases, Nucleic Acid; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D000069550:Machine Learning; D008957:Models, Genetic; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism; D011237:Predictive Value of Tests