{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T14:40:29Z","timestamp":1759848029222},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2016,10,28]],"date-time":"2016-10-28T00:00:00Z","timestamp":1477612800000},"content-version":"vor","delay-in-days":139,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: To understand the dynamic nature of the biological process, it is crucial to identify perturbed pathways in an altered environment and also to infer regulators that trigger the response. Current time-series analysis methods, however, are not powerful enough to identify perturbed pathways and regulators simultaneously. Widely used methods include methods to determine gene sets such as differentially expressed genes or gene clusters and these genes sets need to be further interpreted in terms of biological pathways using other tools. Most pathway analysis methods are not designed for time series data and they do not consider gene-gene influence on the time dimension.<\/jats:p>\n               <jats:p>Results: In this article, we propose a novel time-series analysis method TimeTP for determining transcription factors (TFs) regulating pathway perturbation, which narrows the focus to perturbed sub-pathways and utilizes the gene regulatory network and protein\u2013protein interaction network to locate TFs triggering the perturbation. TimeTP first identifies perturbed sub-pathways that propagate the expression changes along the time. Starting points of the perturbed sub-pathways are mapped into the network and the most influential TFs are determined by influence maximization technique. The analysis result is visually summarized in TF-Pathway map in time clock . TimeTP was applied to PIK3CA knock-in dataset and found significant sub-pathways and their regulators relevant to the PIP3 signaling pathway.<\/jats:p>\n               <jats:p>Availability and Implementation: TimeTP is implemented in Python and available at http:\/\/biohealth.snu.ac.kr\/software\/TimeTP\/ .<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <jats:p>Contact: \u00a0sunkim.bioinfo@snu.ac.kr<\/jats:p>","DOI":"10.1093\/bioinformatics\/btw275","type":"journal-article","created":{"date-parts":[[2016,6,15]],"date-time":"2016-06-15T15:43:52Z","timestamp":1466005432000},"page":"i128-i136","source":"Crossref","is-referenced-by-count":10,"title":["Influence maximization in time bounded network identifies transcription factors regulating perturbed pathways"],"prefix":"10.1093","volume":"32","author":[{"given":"Kyuri","family":"Jo","sequence":"first","affiliation":[{"name":"1 Department of Computer Science and Engineering"}]},{"given":"Inuk","family":"Jung","sequence":"additional","affiliation":[{"name":"2 Interdisciplinary Program in Bioinformatics"}]},{"given":"Ji Hwan","family":"Moon","sequence":"additional","affiliation":[{"name":"2 Interdisciplinary Program in Bioinformatics"}]},{"given":"Sun","family":"Kim","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science and Engineering"},{"name":"2 Interdisciplinary Program in Bioinformatics"},{"name":"3 Bioinformatics Institute, Seoul National University, Seoul 08826, Korea"}]}],"member":"286","published-online":{"date-parts":[[2016,6,11]]},"reference":[{"key":"2023020112305816100_btw275-B1","doi-asserted-by":"crossref","first-page":"i113","DOI":"10.1093\/bioinformatics\/btu274","article-title":"Methods for time series analysis of RNA-seq data with application to human th17 cell differentiation","volume":"30","author":"\u00c4ij\u00f6","year":"2014","journal-title":"Bioinformatics"},{"key":"2023020112305816100_btw275-B2","doi-asserted-by":"crossref","first-page":"10146","DOI":"10.1073\/pnas.1732547100","article-title":"Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes","volume":"100","author":"Bar-Joseph","year":"2003","journal-title":"Proc. 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