{"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/a-bayesian-approach-for-inferring-local","title":"A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks","arxiv_id":"1809.06827","date":"2018-09-18","proceeding":null,"authors":["Ioan Gabriel Bucur","Tom van Bussel","Tom Claassen","Tom Heskes"],"abstract":"Gene regulatory networks play a crucial role in controlling an organism's\nbiological processes, which is why there is significant interest in developing\ncomputational methods that are able to extract their structure from\nhigh-throughput genetic data. A typical approach consists of a series of\nconditional independence tests on the covariance structure meant to\nprogressively reduce the space of possible causal models. We propose a novel\nefficient Bayesian method for discovering the local causal relationships among\ntriplets of (normally distributed) variables. In our approach, we score the\npatterns in the covariance matrix in one go and we incorporate the available\nbackground knowledge in the form of priors over causal structures. Our method\nis flexible in the sense that it allows for different types of causal\nstructures and assumptions. We apply the approach to the task of inferring gene\nregulatory networks by learning regulatory relationships between gene\nexpression levels. We show that our algorithm produces stable and conservative\nposterior probability estimates over local causal structures that can be used\nto derive an honest ranking of the most meaningful regulatory relationships. We\ndemonstrate the stability and efficacy of our method both on simulated data and\non real-world data from an experiment on yeast.","url_abs":"http://arxiv.org/abs/1809.06827v1","url_pdf":"http://arxiv.org/pdf/1809.06827v1.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":"a-bayesian-approach-for-inferring-local","repo_url":"https://github.com/igbucur/BFCS","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}