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Hence, personalised strategies have been proposed to infer patient\u2010specific gene\u2010regulatory networks. However, existing methods do not scale well because they often require recomputing the entire network per sample. Moreover, they do not account for clinically important confounding factors such as age, sex or treatment history. Finally, a user\u2010friendly implementation for the analysis and interpretation of such networks is missing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Experimental Approach<\/jats:title>\n                    <jats:p>We present DysRegNet, a method for inferring patient\u2010specific regulatory alterations (dysregulations) from bulk gene expression profiles. We compared DysRegNet to the well\u2010known\u00a0SSN method, considering patient clustering, promoter methylation, mutations and cancer\u2010stage data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Results<\/jats:title>\n                    <jats:p>\n                      We demonstrate that both SSN and DysRegNet produce interpretable and biologically meaningful networks across various cancer types. In contrast to SSN, DysRegNet can scale to arbitrary sample numbers and highlights the importance of confounders in network inference, revealing an age\u2010specific bias in gene regulation in breast cancer. DysRegNet is available as a Python package (\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/biomedbigdata\/DysRegNet_package\">https:\/\/github.com\/biomedbigdata\/DysRegNet_package<\/jats:ext-link>\n                      ), and analysis results for 11 TCGA cancer types are available through an interactive web interface (\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/exbio.wzw.tum.de\/dysregnet\">https:\/\/exbio.wzw.tum.de\/dysregnet<\/jats:ext-link>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion and Implications<\/jats:title>\n                    <jats:p>DysRegNet introduces a novel bioinformatics tool enabling confounder\u2010aware and patient\u2010specific network analysis to unravel regulatory alteration in complex diseases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>LINKED ARTICLES<\/jats:title>\n                    <jats:p>\n                      This article is part of a themed issue Network Medicine and Systems Pharmacology. To view the other articles in this section visit\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/bph.v183.8\/issuetoc\">http:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/bph.v183.8\/issuetoc<\/jats:ext-link>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/bph.17395","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T19:49:33Z","timestamp":1733341773000},"page":"1709-1724","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DysRegNet: Patient\u2010specific and confounder\u2010aware dysregulated network inference towards precision therapeutics"],"prefix":"10.1111","volume":"183","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3882-0093","authenticated-orcid":false,"given":"Johannes","family":"Kersting","sequence":"first","affiliation":[{"name":"Data 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