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Genes usually form functional modules to play synergistic roles, such as pathways. Predicting significant features from the functional level can effectively reduce the adverse effects of heterogeneity and obtain more reproducible and interpretable biomarkers. Personalized pathway activation inference can quantify the dysregulation of essential pathways involved in the initiation and progression of cancers, and can contribute to the development of personalized medical treatments.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this study, we propose a novel method to evaluate personalized pathway activation based on signaling entropy for survival analysis (SEPA), which is a new attempt to introduce the information-theoretic entropy in generating pathway representation for each patient. SEPA effectively integrates pathway-level information into gene expression data, converting the high-dimensional gene expression data into the low-dimensional biological pathway activation scores. SEPA shows its classification power on the prognostic pan-cancer genomic data, and the potential pathway markers identified based on SEPA have statistical significance in the discrimination of high- and low-risk cohorts and are likely to be associated with the initiation and progress of cancers. The results show that SEPA scores can be used as an indicator to precisely distinguish cancer patients with different clinical outcomes, and identify important pathway features with strong discriminative power and biological interpretability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The MATLAB-package for SEPA is freely available from https:\/\/github.com\/xingyili\/SEPA.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac122","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T12:14:14Z","timestamp":1645445654000},"page":"2536-2543","source":"Crossref","is-referenced-by-count":44,"title":["SEPA: signaling entropy-based algorithm to evaluate personalized pathway activation for survival analysis on pan-cancer data"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6004-4174","authenticated-orcid":false,"given":"Xingyi","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University , Xi\u2019an, Shaanxi 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0188-1394","authenticated-orcid":false,"given":"Min","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Central South University , Changsha, Hunan 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3045-5706","authenticated-orcid":false,"given":"Ju","family":"Xiang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Central South University , Changsha, Hunan 410083, China"},{"name":"Neuroscience Research Center & Department of Basic Medical Sciences, Changsha Medical University , Changsha, Hunan 410219, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhelin","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Software, Northwestern Polytechnical University , Xi\u2019an, Shaanxi 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuequn","family":"Shang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University , Xi\u2019an, Shaanxi 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,2,23]]},"reference":[{"key":"2023041402554061600_","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.anndiagpath.2019.01.002","article-title":"Fatty acid synthase, Her2\/neu, and E2F1 as prognostic markers of progression in non-muscle invasive bladder cancer","volume":"39","author":"Abdelrahman","year":"2019","journal-title":"Ann. 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