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Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles.
- Source :
-
BioMed Research International . 11/24/2016, Vol. 2016, p1-10. 10p. - Publication Year :
- 2016
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *BREAST tumors
*OVARIAN tumors
*TUMOR classification
*TUMOR diagnosis
*TUMOR treatment
*B cell lymphoma
*LUNG tumors
*PROSTATE tumors
*COLON tumors
*ALGORITHMS
*ARTIFICIAL intelligence
*CONFIDENCE intervals
*DECISION trees
*FACTOR analysis
*PROBABILITY theory
*RESEARCH funding
*T-test (Statistics)
*DATA analysis software
*GENE expression profiling
*DESCRIPTIVE statistics
*GENETICS
LEUKEMIA genetics
TUMOR genetics
CENTRAL nervous system tumors
RESEARCH evaluation
Subjects
Details
- Language :
- English
- ISSN :
- 23146133
- Volume :
- 2016
- Database :
- Academic Search Index
- Journal :
- BioMed Research International
- Publication Type :
- Academic Journal
- Accession number :
- 119732270
- Full Text :
- https://doi.org/10.1155/2016/4596326