Cited by 320Genomics and Chromatin DynamicsRNA Research and SplicingBioinformatics and Genomic Networks

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Iterative random forests to discover predictive and stable high-order interactions

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Basu, Sumanta, Kumbier, Karl, Brown, James B., Yu, Bin·2018·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.1711236115PMC PMC5828575

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Abstract

Significance: We developed a predictive, stable, and interpretable tool: the iterative random forest algorithm (iRF). iRF discovers high-order interactions among biomolecules with the same order of computational cost as random forests. We demonstrate the efficacy of iRF by finding known and promising interactions among biomolecules, of up to fifth and sixth order, in two data examples in transcriptional regulation and alternative splicing.

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Fig. 1. iRF workflow. Iteratively reweighted RFs (blue boxes) are trained on full data D and pass Gini importance as weights to the next iteration. In iteration K (red box), feature-weighted RFs are grown using w^(K) on B bootstrap samples of the full data D(1),…,D(B). Decision paths and predicted leaf node labels are passed to the RIT (green box), which computes prevalent interactions in the RF ensemble. Recovered interactions are scored for stability across (outer-layer) bootstrap samples.CC BY-NC-ND · PubMed Central

Fig. 2. (A) Accuracy of iRF (AUC-PR) in predicting active elements from TF binding and histone modification data. (B) The 20 most stable interactions recovered by iRF after five iterations. Interactions that are a strict subset of another interaction with stability score ≥0.5 have been removed for cleaner visualization. iRF recovers known interactions among Gt, Kr, and Hb and interacting roles of master regulator Zld. (C) Surface maps demonstrating the proportion of active enhancers by quantiles of Zld, Gt, and Kr binding (held-out test data). On the subset of data where Kr binding is lower than the median Kr level, the proportion of active enhancers does not change with Gt and Zld. On the subset of data with Kr binding above the median level, the structure of the response surface reflects an order-3 AND interaction: Increased levels of Zld, Gt, and Kr binding are indicative of enhancer status for a subset of observations. (D) Quantiles of Zld, Gt, and Kr binding grouped by enhancer status (balanced sample of held-out test data). The block of active elements highlighted in red represents the subset of observations for which the AND interaction is active.CC BY-NC-ND · PubMed Central

Fig. 3. (A) Accuracy of iRF (AUC-PR) in classifying included exons from excluded exons in held-out test data. iRF shows 7% increase in AUC-PR over RF. (B) An order-6 interaction recovered by iRF (stability score 0.5) displayed on a superheat map which juxtaposes two separately clustered heat maps of exons with high and low splicing rates. Coenrichment of all six plotted features reflects an AND-type rule indicative of high splicing rates for the exons highlighted in red (held-out test data). The subset of Pol II, S2 phospho-Pol II, H3K36me3, H3K79me2, and H4K20me1 was recovered as an order-5 interaction in all bootstrap samples (stability score 1.0). (C) The 20 most stable interactions recovered in the second iteration of iRF. Interactions that are a strict subset of another interaction with stability score ≥0.5 have been removed for cleaner visualization.CC BY-NC-ND · PubMed Central

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Authors: Basu, S., Kumbier, K., Brown, J. B., & Yu, B.
Affiliations: Cornell University (US)
University of California, Berkeley (US)
Lawrence Berkeley National Laboratory (US)
Corresponding author: Brown, James B.
Yu, Bin
ORCID: Basu, Sumanta
Kumbier, Karl
Brown, James B.
Yu, Bin
Venue: Proceedings of the National Academy of Sciences
Publisher: National Academy of Sciences
Published: 2018-01-19
Volume: 115
Issue: 8
Pages: 1943-1948
ISSN: 0027-8424 (ISSN-L)
1091-6490
DOI: 10.1073/pnas.1711236115
PMC: PMC5828575
OpenAlex: W2731142262
Cited by: 317
References: 80
Citations by year: 2026: 24 · 2025: 32 · 2024: 39 · 2023: 51 · 2022: 48 · 2021: 42 · 2020: 46 · 2019: 20 · 2018: 12
FWCI: 10.76
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
eScholarship (California Digital Library) (submittedVersion)
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
arXiv (Cornell University)
Funders: HHS | NIH | National Human Genome Research Institute (U01HG007031, R00 HG006698)
DOD | United States Army | RDECOM | Army Research Office (W911NF1710005)
DOD | United States Navy | Office of Naval Research (N00014-16-1- 2664)
U.S. Department of Energy (DOE DE-AC02-05CH11231, DE-SC0017069)
National Science Foundation (CCF-0939370)
HHS | NIH |U.S. National Library of Medicine (T32LM012417)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: higher-order interactions, epistasis, H3K36me3, epigenetic interactions
MeSH terms: Algorithms, Animals, Drosophila, Models, Genetic, Alternative Splicing, Gene Expression Regulation, Developmental, Computational Biology, Gene Regulatory Networks, Genome-Wide Association Study

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Iterative random forests to discover predictive and stable high-order interactions

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