Gene selection using iterative feature elimination random forests for survival outcomes.
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Although many feature selection methods for classification have been developed, there is a need to identify genes in high-dimensional data with censored survival outcomes. Traditional methods for gene selection in classification problems have several drawbacks. First, the majority of the gene selection approaches for classification are single-gene based. Second, many of the gene selection procedures are not embedded within the algorithm itself. The technique of random forests has been found to perform well in high-dimensional data settings with survival outcomes. It also has an embedded feature to identify variables of importance. Therefore, it is an ideal candidate for gene selection in high-dimensional data with survival outcomes. In this paper, we develop a novel method based on the random forests to identify a set of prognostic genes. We compare our method with several machine learning methods and various node split criteria using several real data sets. Our method performed well in both simulations and real data analysis.Additionally, we have shown the advantages of our approach over single-gene-based approaches. Our method incorporates multivariate correlations in microarray data for survival outcomes. The described method allows us to better utilize the information available from microarray data with survival outcomes.
Gene Expression Profiling
Oligonucleotide Array Sequence Analysis
Pattern Recognition, Automated
Published Version (Please cite this version)10.1109/TCBB.2012.63
Publication InfoPang, Herbert; George, Stephen L; Hui, Ken; & Tong, Tiejun (2012). Gene selection using iterative feature elimination random forests for survival outcomes. IEEE/ACM Trans Comput Biol Bioinform, 9(5). pp. 1422-1431. 10.1109/TCBB.2012.63. Retrieved from https://hdl.handle.net/10161/9228.
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Professor Emeritus of Biostatistics & Bioinformatics
Statistical issues related to the design, conduct, and analysis of clinical trials and related biomedical studies including sample size and study length determinations, sequential procedures, and the analysis of prognostic or predictive factors in clinical trials.
Adjunct Assistant Professor in the Department of Biostatistics & Bioinformatics
Classification and Predictive Models Design and Analysis of Biomarker Clinical Trials Genomics Pathway Analysis
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