ArticleBioinformatics (Oxford, England)2015
Integrative random forest for gene regulatory network inference.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers.
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Who cites it
96 citing papers in PubMed.
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- Inference of Genetic Networks from Pseudo Time Series of Single-cell Gene Expression Data using Modified Random Forests.Bulletin of mathematical biology · 2026Article
- Integrated Gene Regulatory Network Analysis Reveals Coordinated Transcriptional Reprogramming in thePlants (Basel, Switzerland) · 2026Article
- HSTXGB: a hyperparameter self-tuning XGBoost method integrating pre- and post-processing for gene regulatory network inference.Frontiers in genetics · 2026Article
- From binding to networks: methods for identifying transcription factor targets in plant systems.Frontiers in plant science · 2026Review
- Network-informed deconvolution of bulk immune gene co-expression reveals single-cell programs and spatial organization.Frontiers in immunology · 2026Article
- Machine learning methods for gene regulatory network inference.Briefings in bioinformatics · 2025Review
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- Identification of hub genes for the diagnosis associated with heart failure using multiple cell death patterns.ESC heart failure · 2025Article
- Integrating bioinformatics and machine learning to investigate the mechanisms by which three major respiratory infectious diseases exacerbate heart failure.Scientific reports · 2025Article
- Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy.Scientific reports · 2025Article
- Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.Molecular systems biology · 2025Review
- Explainable artificial intelligence of DNA methylation-based brain tumor diagnostics.Nature communications · 2025Article
- Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping.BioData mining · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Inference of Gene Regulatory Networks for Breast Cancer Based on Genetic Modules.BME frontiers · 2025Article
- Uncovering key biomarkers, potential therapeutic targets and development of deep learning model in heart failure.PloS one · 2025Article
- Article
- Integrating machine learning and multi-omics analysis to develop an asparagine metabolism immunity index for improving clinical outcome and drug sensitivity in lung adenocarcinoma.Immunologic research · 2024Article
36 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
Abstract
motivationGene regulatory network (GRN) inference based on genomic data is one of the most actively pursued computational biological problems. Because different types of biological data usually provide complementary information regarding the underlying GRN, a model that integrates big data of diverse types is expected to increase both the power and accuracy of GRN inference. Towards this goal, we propose a novel algorithm named iRafNet: integrative random forest for gene regulatory network inference.
resultsiRafNet is a flexible, unified integrative framework that allows information from heterogeneous data, such as protein-protein interactions, transcription factor (TF)-DNA-binding, gene knock-down, to be jointly considered for GRN inference. Using test data from the DREAM4 and DREAM5 challenges, we demonstrate that iRafNet outperforms the original random forest based network inference algorithm (GENIE3), and is highly comparable to the community learning approach. We apply iRafNet to construct GRN in Saccharomyces cerevisiae and demonstrate that it improves the performance in predicting TF-target gene regulations and provides additional functional insights to the predicted gene regulations. AVAILABILITY AND IMPLEMENTATION: The R code of iRafNet implementation and a tutorial are available at: http://research.mssm.edu/tulab/software/irafnet.html
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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.