{"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/estimating-sample-specific-regulatory","title":"Estimating sample-specific regulatory networks","arxiv_id":"1505.06440","date":"2015-05-24","proceeding":null,"authors":["Marieke Lydia Kuijjer","Matthew Tung","Guo-Cheng Yuan","John Quackenbush","Kimberly Glass"],"abstract":"Biological systems are driven by intricate interactions among the complex\narray of molecules that comprise the cell. Many methods have been developed to\nreconstruct network models of those interactions. These methods often draw on\nlarge numbers of samples with measured gene expression profiles to infer\nconnections between genes (or gene products). The result is an aggregate\nnetwork model representing a single estimate for the likelihood of each\ninteraction, or \"edge,\" in the network. While informative, aggregate models\nfail to capture the heterogeneity that is represented in any population. Here\nwe propose a method to reverse engineer sample-specific networks from aggregate\nnetwork models. We demonstrate the accuracy and applicability of our approach\nin several data sets, including simulated data, microarray expression data from\nsynchronized yeast cells, and RNA-seq data collected from human lymphoblastoid\ncell lines. We show that these sample-specific networks can be used to study\nchanges in network topology across time and to characterize shifts in gene\nregulation that may not be apparent in expression data. We believe the ability\nto generate sample-specific networks will greatly facilitate the application of\nnetwork methods to the increasingly large, complex, and heterogeneous\nmulti-omic data sets that are currently being generated, and ultimately support\nthe emerging field of precision network medicine.","url_abs":"http://arxiv.org/abs/1505.06440v2","url_pdf":"http://arxiv.org/pdf/1505.06440v2.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":"estimating-sample-specific-regulatory","repo_url":"https://github.com/aless80/PyPuma","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"estimating-sample-specific-regulatory","repo_url":"https://github.com/mararie/lionessR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"estimating-sample-specific-regulatory","repo_url":"https://github.com/twangxxx/netZooR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}