Issue No. 05 - Sept.-Oct. (2012 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.63
Herbert Pang , Sch. of Med., Dept. of Biostat. & Bioinf., Duke Univ., Durham, NC, USA
Stephen L. George , Sch. of Med., Dept. of Biostat. & Bioinf., Duke Univ., Durham, NC, USA
Ken Hui , Sch. of Med., Dept. of Internal Med., Yale Univ., New Haven, CT, USA
Tiejun Tong , Dept. of Math., Hong Kong Baptist Univ., Kowloon Tong, China
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.
pattern classification, biology computing, feature extraction, genetics, genomics, iterative methods, lab-on-a-chip, learning (artificial intelligence), microarray data, gene selection, iterative feature elimination random forests, feature selection methods, censored survival outcomes, classification problems, single-gene based classification, high-dimensional data settings, machine learning methods, node split criteria, Cancer, Genetics, Feature extraction, Random processes, Iterative methods, survival., Cancer, gene selection, iterative feature elimination, microarrays, random forest
K. Hui, S. L. George, H. Pang and Tiejun Tong, "Gene Selection Using Iterative Feature Elimination Random Forests for Survival Outcomes," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. , pp. 1422-1431, 2012.