|Title||Gene selection using iterative feature elimination random forests for survival outcomes.|
|Publication Type||Journal Article|
|Year of Publication||2012|
|Authors||Pang, Herbert, Stephen L. George, Ken Hui, and Tiejun Tong|
|Journal||IEEE/ACM Trans Comput Biol Bioinform|
|Date Published||2012 Sep-Oct|
|Keywords||Algorithms, Artificial Intelligence, Gene Expression Profiling, Oligonucleotide Array Sequence Analysis, Pattern Recognition, Automated|
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.
|Alternate Journal||IEEE/ACM Trans Comput Biol Bioinform|
|Original Publication||Gene selection using iterative feature elimination random forests for survival outcomes.|
|PubMed Central ID||PMC3495190|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
T32 GM007205 / GM / NIGMS NIH HHS / United States
T32GM07205 / GM / NIGMS NIH HHS / United States
P01CA142538 / CA / NCI NIH HHS / United States
Gene selection using iterative feature elimination random forests for survival outcomes.