ArticleIEEE/ACM transactions on computational biology and bioinformatics
Gene selection using iterative feature elimination random forests for survival outcomes.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Prognostic biomarker discovery in pancreatic cancer through hybrid ensemble feature selection and multi-omics data.BioData mining · 2026Article
- Random survival forests for the analysis of recurrent events for right-censored data, with or without a terminal event.BMC medical research methodology · 2025Article
- Improved nonparametric survival prediction using CoxPH, Random Survival Forest & DeepHit Neural Network.BMC medical informatics and decision making · 2024Article
- Rationally designed probiotics prevent shrimp white feces syndrome via the probiotics-gut microbiome-immunity axis.NPJ biofilms and microbiomes · 2024Article
- A network approach for low dimensional signatures from high throughput data.Scientific reports · 2022Article
- Random survival forest model identifies novel biomarkers of event-free survival in high-risk pediatric acute lymphoblastic leukemia.Computational and structural biotechnology journal · 2022Article
- An Efficient Cancer Classification Model Using Microarray and High-Dimensional Data.Computational intelligence and neuroscience · 2021Article
- Personalized prediction of delayed graft function for recipients of deceased donor kidney transplants with machine learning.Scientific reports · 2020Article
- Detecting biomarkers from microarray data using distributed correlation based gene selection.Genes & genomics · 2020Article
- Multiplatform biomarker identification using a data-driven approach enables single-sample classification.BMC bioinformatics · 2019Article
- Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.BMC medical informatics and decision making · 2017Article
- RGIFE: a ranked guided iterative feature elimination heuristic for the identification of biomarkers.BMC bioinformatics · 2017Article
- A Selective Review on Random Survival Forests for High Dimensional Data.Quantitative bio-science · 2017Article
- Statistical aspect of translational and correlative studies in clinical trials.Chinese clinical oncology · 2016Article
- Stratified pathway analysis to identify gene sets associated with oral contraceptive use and breast cancer.Cancer informatics · 2014Article
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Authors and funding
4 authors.
Funding
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.
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