Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles.
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 h...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 11 |
|---|---|
| Autores principales: | , , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
11/24/2016
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=119732270&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 119732270 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 11/24/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 119732270 119732270 119732270 10.1155/2016/4596326 119732270 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles. aug: au: Yang, Liying Liu, Zhimin Yuan, Xiguo Wei, Jianhua Zhang, Junying affil: School of Computer Science and Technology, Xidian University, Xi’an, Shaanxi 710071, China sug: subj: Gene Expression Profiling Evaluation Neoplasms Familial and Genetic Neoplasms Diagnosis Neoplasms Therapy Gene Expression Profiling Methods Descriptive Statistics Artificial Intelligence Neoplasms Classification Validity Decision Trees Algorithms Breast Neoplasms Familial and Genetic Leukemia Familial and Genetic Lung Neoplasms Familial and Genetic Prostatic Neoplasms Familial and Genetic Colonic Neoplasms Familial and Genetic Ovarian Neoplasms Familial and Genetic Central Nervous System Neoplasms Familial and Genetic T-Tests P-Value Data Analysis Software Factor Analysis Lymphoma, B-Cell Familial and Genetic Confidence Intervals Funding Source ab: 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. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|