{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/refining-interaction-search-through-signed","title":"Signed iterative random forests to identify enhancer-associated transcription factor binding","arxiv_id":"1810.07287","date":"2018-10-16","proceeding":null,"authors":["Karl Kumbier","Sumanta Basu","Erwin Frise","Susan E. Celniker","James B. Brown","Susan Celniker","Bin Yu"],"abstract":"Standard ChIP-seq peak calling pipelines seek to differentiate biochemically reproducible signals of individual genomic elements from background noise. However, reproducibility alone does not imply functional regulation (e.g., enhancer activation, alternative splicing). Here we present a general-purpose, interpretable machine learning method: signed iterative random forests (siRF), which we use to infer regulatory interactions among transcription factors and functional binding signatures surrounding enhancer elements in Drosophila melanogaster.","url_abs":"https://arxiv.org/abs/1810.07287v2","url_pdf":"https://arxiv.org/pdf/1810.07287v2.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":"refining-interaction-search-through-signed","repo_url":"https://github.com/sumbose/iRF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"interpretable-machine-learning","task_name":"Interpretable Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}