{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T19:14:06Z","timestamp":1781118846831,"version":"3.54.1"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Most current approaches to high-throughput biological data (HTBD) analysis either perform individual gene\/protein analysis or, gene\/protein set enrichment analysis for a list of biologically relevant molecules. Bayesian Networks (BNs) capture linear and non-linear interactions, handle stochastic events accounting for noise, and focus on local interactions, which can be related to causal inference. Here, we describe for the first time an algorithm that models biological pathways as BNs and identifies pathways that best explain given HTBD by scoring fitness of each network.<\/jats:p><jats:p>Results: Proposed method takes into account the connectivity and relatedness between nodes of the pathway through factoring pathway topology in its model. Our simulations using synthetic data demonstrated robustness of our approach. We tested proposed method, Bayesian Pathway Analysis (BPA), on human microarray data regarding renal cell carcinoma (RCC) and compared our results with gene set enrichment analysis. BPA was able to find broader and more specific pathways related to RCC.<\/jats:p><jats:p>Availability: Accompanying BPA software (BPAS) package is freely available for academic use at http:\/\/bumil.boun.edu.tr\/bpa.<\/jats:p><jats:p>Contact: \u00a0hotu@bidmc.harvard.edu<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btr269","type":"journal-article","created":{"date-parts":[[2011,5,7]],"date-time":"2011-05-07T00:34:56Z","timestamp":1304728496000},"page":"1667-1674","source":"Crossref","is-referenced-by-count":36,"title":["Pathway analysis of high-throughput biological data within a Bayesian network framework"],"prefix":"10.1093","volume":"27","author":[{"given":"Senol","family":"Isci","sequence":"first","affiliation":[{"name":"1 Bogazici University, Institute of Biomedical Engineering, 34342, Istanbul, Turkey, 2Department of Urology, Johannes Gutenberg University, 55131 Mainz, Germany, 3Department of Bioengineering, Istanbul Bilgi University, 34060, Istanbul, Turkey and 4Department of Medicine, BIDMC Genomics Center, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cengizhan","family":"Ozturk","sequence":"additional","affiliation":[{"name":"1 Bogazici University, Institute of Biomedical Engineering, 34342, Istanbul, Turkey, 2Department of Urology, Johannes Gutenberg University, 55131 Mainz, Germany, 3Department of Bioengineering, Istanbul Bilgi University, 34060, Istanbul, Turkey and 4Department of Medicine, BIDMC Genomics Center, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jon","family":"Jones","sequence":"additional","affiliation":[{"name":"1 Bogazici University, Institute of Biomedical Engineering, 34342, Istanbul, Turkey, 2Department of Urology, Johannes Gutenberg University, 55131 Mainz, Germany, 3Department of Bioengineering, Istanbul Bilgi University, 34060, Istanbul, Turkey and 4Department of Medicine, BIDMC Genomics Center, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hasan H.","family":"Otu","sequence":"additional","affiliation":[{"name":"1 Bogazici University, Institute of Biomedical Engineering, 34342, Istanbul, Turkey, 2Department of Urology, Johannes Gutenberg University, 55131 Mainz, Germany, 3Department of Bioengineering, Istanbul Bilgi University, 34060, Istanbul, Turkey and 4Department of Medicine, BIDMC Genomics Center, Harvard Medical School, Boston, MA 02115, USA"},{"name":"1 Bogazici University, Institute of Biomedical Engineering, 34342, Istanbul, Turkey, 2Department of Urology, Johannes Gutenberg University, 55131 Mainz, Germany, 3Department of Bioengineering, Istanbul Bilgi University, 34060, Istanbul, Turkey and 4Department of Medicine, BIDMC Genomics Center, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2011,5,5]]},"reference":[{"key":"2023012511144688600_B1","doi-asserted-by":"crossref","first-page":"1600","DOI":"10.1093\/bioinformatics\/btl140","article-title":"Improved scoring of functional groups from gene expression data by decorrelating GO graph structure","volume":"22","author":"Alexa","year":"2006","journal-title":"Bioinformatics"},{"key":"2023012511144688600_B2","doi-asserted-by":"crossref","first-page":"3523","DOI":"10.1093\/nar\/gkq045","article-title":"GOing Bayesian: model-based gene set analysis of genome-scale data","volume":"38","author":"Bauer","year":"2010","journal-title":"Nucleic Acids Res."},{"key":"2023012511144688600_B3","first-page":"247","article-title":"The ALARM monitoring system: a case study with two probabilistic inference techniques for belief networks","volume-title":"Proceedings of the Second European Conference on Artificial Intelligence in Medicine","author":"Beinlich","year":"1989"},{"key":"2023012511144688600_B4","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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