{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T02:18:51Z","timestamp":1767925131092,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,6,5]],"date-time":"2019-06-05T00:00:00Z","timestamp":1559692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["#1651917 (Transferred to #1901628)"],"award-info":[{"award-number":["#1651917 (Transferred to #1901628)"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Two graph theoretic concepts\u2014clique and bipartite graphs\u2014are explored to identify the network biomarkers for cancer at the gene network level. The rationale is that a group of genes work together by forming a cluster or a clique-like structures to initiate a cancer. After initiation, the disease signal goes to the next group of genes related to the second stage of a cancer, which can be represented as a bipartite graph. In other words, bipartite graphs represent the cross-talk among the genes between two disease stages. To prove this hypothesis, gene expression values for three cancers\u2014 breast invasive carcinoma (BRCA), colorectal adenocarcinoma (COAD) and glioblastoma multiforme (GBM)\u2014are used for analysis. First, a co-expression gene network is generated with highly correlated gene pairs with a Pearson correlation coefficient \u2265 0.9. Second, clique structures of all sizes are isolated from the co-expression network. Then combining these cliques, three different biomarker modules are developed\u2014maximal clique-like modules, 2-clique-1-bipartite modules, and 3-clique-2-bipartite modules. The list of biomarker genes discovered from these network modules are validated as the essential genes for causing a cancer in terms of network properties and survival analysis. This list of biomarker genes will help biologists to design wet lab experiments for further elucidating the complex mechanism of cancer.<\/jats:p>","DOI":"10.3390\/data4020081","type":"journal-article","created":{"date-parts":[[2019,6,6]],"date-time":"2019-06-06T03:38:01Z","timestamp":1559792281000},"page":"81","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Graph Theoretic and Pearson Correlation-Based Discovery of Network Biomarkers for Cancer"],"prefix":"10.3390","volume":"4","author":[{"given":"Raihanul Bari","family":"Tanvir","sequence":"first","affiliation":[{"name":"School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA"}]},{"given":"Tasmia","family":"Aqila","sequence":"additional","affiliation":[{"name":"School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA"}]},{"given":"Mona","family":"Maharjan","sequence":"additional","affiliation":[{"name":"School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA"}]},{"given":"Abdullah Al","family":"Mamun","sequence":"additional","affiliation":[{"name":"School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA"}]},{"given":"Ananda Mohan","family":"Mondal","sequence":"additional","affiliation":[{"name":"School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1101\/gr.071852.107","article-title":"Protein networks in disease","volume":"18","author":"Ideker","year":"2008","journal-title":"Genome Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1126\/science.1195618","article-title":"Rewiring of Genetic Networks in Response to DNA Damage","volume":"330","author":"Bandyopadhyay","year":"2010","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"R95","DOI":"10.1186\/gb-2004-5-12-r95","article-title":"Integrating phenotypic and expression profiles to map arsenic-response networks","volume":"5","author":"Haugen","year":"2004","journal-title":"Genome Boil."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1089\/omi.2006.10.40","article-title":"Diffusion Kernel-Based Logistic Regression Models for Protein Function Prediction","volume":"10","author":"Lee","year":"2006","journal-title":"OMICS A J. 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