{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T13:36:37Z","timestamp":1770903397368,"version":"3.50.1"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Summary: Characterization of biological processes is progressively enabled with the increased generation of omics data on different signaling levels. Here we present a straightforward approach for the integrative analysis of data from different high-throughput technologies based on pathway and interaction models from public databases. pwOmics performs pathway-based level-specific data comparison of coupled human proteomic and genomic\/transcriptomic datasets based on their log fold changes. Separate downstream and upstream analyses results on the functional levels of pathways, transcription factors and genes\/transcripts are performed in the cross-platform consensus analysis. These provide a basis for the combined interpretation of regulatory effects over time. Via network reconstruction and inference methods (Steiner tree, dynamic Bayesian network inference) consensus graphical networks can be generated for further analyses and visualization.<\/jats:p>\n               <jats:p>Availability and implementation: The R package pwOmics is freely available on Bioconductor (http:\/\/www.bioconductor.org\/).<\/jats:p>\n               <jats:p>Contact: \u00a0astrid.wachter@med.uni-goettingen.de<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv323","type":"journal-article","created":{"date-parts":[[2015,5,23]],"date-time":"2015-05-23T00:50:24Z","timestamp":1432342224000},"page":"3072-3074","source":"Crossref","is-referenced-by-count":29,"title":["pwOmics: an R package for pathway-based integration of time-series omics data using public database knowledge"],"prefix":"10.1093","volume":"31","author":[{"given":"Astrid","family":"Wachter","sequence":"first","affiliation":[{"name":"Department of Medical Statistics, Georg-August-University G\u00f6ttingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim","family":"Bei\u00dfbarth","sequence":"additional","affiliation":[{"name":"Department of Medical Statistics, Georg-August-University G\u00f6ttingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2015,5,21]]},"reference":[{"key":"2023020202222796500_btv323-B1","doi-asserted-by":"crossref","first-page":"D472","DOI":"10.1093\/nar\/gkt1102","article-title":"The Reactome pathway knowledgebase","volume":"42","author":"Croft","year":"2014","journal-title":"Nucleic Acids Res."},{"key":"2023020202222796500_btv323-B2","doi-asserted-by":"crossref","first-page":"D808","DOI":"10.1093\/nar\/gks1094","article-title":"STRING v9.1: protein\u2013protein interaction networks, with increased coverage and integration","volume":"41","author":"Franceschini","year":"2013","journal-title":"Nucleic Acids Res."},{"key":"2023020202222796500_btv323-B3","doi-asserted-by":"crossref","first-page":"2917","DOI":"10.1093\/bioinformatics\/btr499","article-title":"Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA","volume":"27","author":"Kamburov","year":"2011","journal-title":"Bioinformatics"},{"key":"2023020202222796500_btv323-B4","doi-asserted-by":"crossref","first-page":"D199","DOI":"10.1093\/nar\/gkt1076","article-title":"Data, information, knowledge and principle: back to metabolism in KEGG","volume":"42","author":"Kanehisa","year":"2014","journal-title":"Nucleic Acids Res."},{"key":"2023020202222796500_btv323-B5","doi-asserted-by":"crossref","first-page":"re1","DOI":"10.1126\/scisignal.2002961","article-title":"Computational approaches for analyzing information flow in biological networks","volume":"5","author":"Kholodenko","year":"2012","journal-title":"Sci. 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