ArticleBioinformatics (Oxford, England)2013
Pathway hunting by random survival forests.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Article
- Enhancement of anti-cancer compounds in fungal elicited-Oldenlandia umbellata culture.Naunyn-Schmiedeberg's archives of pharmacology · 2024Article
- A robust ensemble feature selection approach to prioritize genes associated with survival outcome in high-dimensional gene expression data.Frontiers in systems biology · 2024Article
- Identification of a Novel Oxidative Stress- and Anoikis-Related Prognostic Signature and Its Immune Landscape Analysis in Non-Small Cell Lung Cancer.International journal of molecular sciences · 2023Article
- Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence.International journal of molecular sciences · 2021Review
- Random survival forests identify pathways with polymorphisms predictive of survival in KRAS mutant and KRAS wild-type metastatic colorectal cancer patients.Scientific reports · 2021Article
- A Machine Learning-Based Model to Predict Survival After Transarterial Chemoembolization for BCLC Stage B Hepatocellular Carcinoma.Frontiers in oncology · 2021Article
- LR Hunting: A Random Forest Based Cell-Cell Interaction Discovery Method for Single-Cell Gene Expression Data.Frontiers in genetics · 2021Article
- Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking study.Bioinformatics (Oxford, England) · 2020Article
- A 5-Gene Prognostic Combination for Predicting Survival of Patients with Gastric Cancer.Medical science monitor : international medical journal of experimental and clinical research · 2019Article
- Integration of gene interaction information into a reweighted random survival forest approach for accurate survival prediction and survival biomarker discovery.Scientific reports · 2018Article
- Prognostic value of cancer antigen -125 for lung adenocarcinoma patients with brain metastasis: A random survival forest prognostic model.Scientific reports · 2018Article
- L₁ splitting rules in survival forests.Lifetime data analysis · 2017Article
- Hemoglobin level, a prognostic factor for nasal extranodal natural killer/T-cell lymphoma patients from stage I to IV: A validated prognostic nomogram.Scientific reports · 2017Article
- 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
- A Selective Review on Random Survival Forests for High Dimensional Data.Quantitative bio-science · 2017Article
- A Clinical Decision Tree to Predict Whether a Bacteremic Patient Is Infected With an Extended-Spectrum β-Lactamase-Producing Organism.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2016Article
- Article
- Mining gene link information for survival pathway hunting.IET systems biology · 2015Article
- Letter to the Editor: On the term 'interaction' and related phrases in the literature on Random Forests.Briefings in bioinformatics · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
motivationPathway or gene set analysis has been widely applied to genomic data. Many current pathway testing methods use univariate test statistics calculated from individual genomic markers, which ignores the correlations and interactions between candidate markers. Random forests-based pathway analysis is a promising approach for incorporating complex correlation and interaction patterns, but one limitation of previous approaches is that pathways have been considered separately, thus pathway cross-talk information was not considered.
resultsIn this article, we develop a new pathway hunting algorithm for survival outcomes using random survival forests, which prioritize important pathways by accounting for gene correlation and genomic interactions. We show that the proposed method performs favourably compared with five popular pathway testing methods using both synthetic and real data. We find that the proposed methodology provides an efficient and powerful pathway modelling framework for high-dimensional genomic data. AVAILABILITY: The R code for the analysis used in this article is available upon request.
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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.