ArticleBioinformatics (Oxford, England)2010
Pathway analysis using random forests with bivariate node-split for survival outcomes.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on Machine Learning Techniques for Survival Analysis in Cancer.Cancer medicine · 2025Pooled it
- Development and validation of a quantitative real-time polymerase chain reaction classifier for lung cancer prognosis.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2011Trial
- Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake.International journal of environmental research and public health · 2022Article
- Radiomics analysis using stability selection supervised component analysis for right-censored survival data.Computers in biology and medicine · 2020Article
- Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China).International journal of environmental research and public health · 2020Article
- Integration of gene interaction information into a reweighted random survival forest approach for accurate survival prediction and survival biomarker discovery.Scientific reports · 2018Article
- Analysis of a large data set to identify predictors of blood transfusion in primary total hip and knee arthroplasty.Transfusion · 2018Article
- Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.Clinical and translational science · 2018Article
- Survival Forests with R-Squared Splitting Rules.Journal of computational biology : a journal of computational molecular cell biology · 2018Article
- Article
- Statistical aspect of translational and correlative studies in clinical trials.Chinese clinical oncology · 2016Article
- Random Effects Model for Multiple Pathway Analysis with Applications to Type II Diabetes Microarray Data.Statistics in biosciences · 2015Article
- Mining gene link information for survival pathway hunting.IET systems biology · 2015Article
- Extending information retrieval methods to personalized genomic-based studies of disease.Cancer informatics · 2014Article
- Stratified pathway analysis to identify gene sets associated with oral contraceptive use and breast cancer.Cancer informatics · 2014Article
- Integrative Pathway Analysis Using Graph-Based Learning with Applications to TCGA Colon and Ovarian Data.Cancer informatics · 2014Article
- Sample size considerations of prediction-validation methods in high-dimensional data for survival outcomes.Genetic epidemiology · 2013Article
- Pathway hunting by random survival forests.Bioinformatics (Oxford, England) · 2013Article
- Random forests for genomic data analysis.Genomics · 2012Review
- Pathway-based identification of SNPs predictive of survival.European journal of human genetics : EJHG · 2011Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
motivationThere is great interest in pathway-based methods for genomics data analysis in the research community. Although machine learning methods, such as random forests, have been developed to correlate survival outcomes with a set of genes, no study has assessed the abilities of these methods in incorporating pathway information for analyzing microarray data. In general, genes that are identified without incorporating biological knowledge are more difficult to interpret. Correlating pathway-based gene expression with survival outcomes may lead to biologically more meaningful prognosis biomarkers. Thus, a comprehensive study on how these methods perform in a pathway-based setting is warranted.
resultsIn this article, we describe a pathway-based method using random forests to correlate gene expression data with survival outcomes and introduce a novel bivariate node-splitting random survival forests. The proposed method allows researchers to identify important pathways for predicting patient prognosis and time to disease progression, and discover important genes within those pathways. We compared different implementations of random forests with different split criteria and found that bivariate node-splitting random survival forests with log-rank test is among the best. We also performed simulation studies that showed random forests outperforms several other machine learning algorithms and has comparable results with a newly developed component-wise Cox boosting model. Thus, pathway-based survival analysis using machine learning tools represents a promising approach in dissecting pathways and for generating new biological hypothesis from microarray studies. AVAILABILITY: R package Pwayrfsurvival is available from URL: http://www.duke.edu/~hp44/pwayrfsurvival.htm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.