{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T17:10:25Z","timestamp":1763831425234,"version":"3.41.2"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"content-version":"vor","delay-in-days":38,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Network-based methods utilize protein\u2013protein interaction information to identify significantly perturbed subnetworks in cancer and to propose key molecular pathways. Numerous methods have been developed, but to date, a rigorous benchmark analysis to compare the performance of existing approaches is lacking. In this paper, we proposed a novel benchmarking framework using synthetic data and conducted a comprehensive analysis to investigate the ability of existing methods to detect target genes and subnetworks and to control false positives, and how they perform in the presence of topological biases at both gene and subnetwork levels. Our analysis revealed insights into algorithmic performance that were previously unattainable. Based on the results of the benchmark study, we presented a practical guide for users on how to select appropriate detection methods and protein\u2013protein interaction networks for cancer pathway identification, and provided suggestions for future algorithm development.<\/jats:p>","DOI":"10.1093\/bib\/bbae692","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T01:29:38Z","timestamp":1735608578000},"source":"Crossref","is-referenced-by-count":2,"title":["A comprehensive benchmark study of methods for identifying significantly perturbed subnetworks in cancer"],"prefix":"10.1093","volume":"26","author":[{"given":"Le","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Microbiology and Immunology, University at Buffalo, The State University of New York , 955 Main Street, Buffalo, New York, NY 14203 ,","place":["United States"]}]},{"given":"Runpu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Microbiology and Immunology, University at Buffalo, The State University of New York , 955 Main Street, Buffalo, New York, NY 14203 ,","place":["United States"]}]},{"given":"Steve","family":"Goodison","sequence":"additional","affiliation":[{"name":"Department of Quantitative Health Sciences, Mayo Clinic , 4500 San Pablo Rd S, Jacksonville, FL 32224 ,","place":["United States"]}]},{"given":"Yijun","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Microbiology and Immunology, University at Buffalo, The State University of New York , 955 Main Street, Buffalo, New York, NY 14203 ,","place":["United States"]},{"name":"Department of Computer Science and Engineering, University at Buffalo, The State University of New York , 12 Capen Hall, Buffalo, New York, NY 14260 ,","place":["United 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