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Gene selection using iterative feature elimination random forests for survival outcomes.

dc.contributor.author George, Stephen L
dc.contributor.author Hui, K
dc.contributor.author Pang, H
dc.contributor.author Tong, T
dc.coverage.spatial United States
dc.date.accessioned 2014-11-07T20:03:27Z
dc.date.issued 2012-09
dc.identifier http://www.ncbi.nlm.nih.gov/pubmed/22547432
dc.identifier.uri https://hdl.handle.net/10161/9228
dc.description.abstract 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.
dc.language eng
dc.relation.ispartof IEEE/ACM Trans Comput Biol Bioinform
dc.relation.isversionof 10.1109/TCBB.2012.63
dc.subject Algorithms
dc.subject Artificial Intelligence
dc.subject Gene Expression Profiling
dc.subject Oligonucleotide Array Sequence Analysis
dc.subject Pattern Recognition, Automated
dc.title Gene selection using iterative feature elimination random forests for survival outcomes.
dc.type Journal article
pubs.author-url http://www.ncbi.nlm.nih.gov/pubmed/22547432
pubs.begin-page 1422
pubs.end-page 1431
pubs.issue 5
pubs.organisational-group Basic Science Departments
pubs.organisational-group Biostatistics & Bioinformatics
pubs.organisational-group Duke
pubs.organisational-group School of Medicine
pubs.publication-status Published
pubs.volume 9
dc.identifier.eissn 1557-9964


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