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However, individual genomic assays measure elements that\ninteract in vivo as components of larger molecular machines. Understanding how\nthese high-order interactions drive gene expression presents a substantial\nstatistical challenge. Building on Random Forests (RF), Random Intersection\nTrees (RITs), and through extensive, biologically inspired simulations, we\ndeveloped the iterative Random Forest algorithm (iRF). iRF trains a\nfeature-weighted ensemble of decision trees to detect stable, high-order\ninteractions with same order of computational cost as RF. We demonstrate the\nutility of iRF for high-order interaction discovery in two prediction problems:\nenhancer activity in the early Drosophila embryo and alternative splicing of\nprimary transcripts in human derived cell lines. In Drosophila, among the 20\npairwise transcription factor interactions iRF identifies as stable (returned\nin more than half of bootstrap replicates), 80% have been previously reported\nas physical interactions. Moreover, novel third-order interactions, e.g.\nbetween Zelda (Zld), Giant (Gt), and Twist (Twi), suggest high-order\nrelationships that are candidates for follow-up experiments. In human-derived\ncells, iRF re-discovered a central role of H3K36me3 in chromatin-mediated\nsplicing regulation, and identified novel 5th and 6th order interactions,\nindicative of multi-valent nucleosomes with specific roles in splicing\nregulation. By decoupling the order of interactions from the computational cost\nof identification, iRF opens new avenues of inquiry into the molecular\nmechanisms underlying genome biology.","url_abs":"http://arxiv.org/abs/1706.08457v4","url_pdf":"http://arxiv.org/pdf/1706.08457v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"iterative-random-forests-to-detect-predictive","repo_url":"https://github.com/Yu-Group/iterative-Random-Forest","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"iterative-random-forests-to-detect-predictive","repo_url":"https://github.com/bpark738/iRF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"iterative-random-forests-to-detect-predictive","repo_url":"https://github.com/ishoxo/irf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"iterative-random-forests-to-detect-predictive","repo_url":"https://github.com/shifwang/irf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1706.08457","atlas_url":"https://app.syntology.ai/?focus=1706.08457","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.08457"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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