EnsemBic: An effective ensemble of biclustering to identify potential biomarkers of esophageal squamous cell carcinoma
The development of functionally enriched and biologically competent biclustering algorithm is essential for extracting hidden information from massive biological datasets. This paper presents a novel biclustering ensemble called EnsemBic based on p-value, which calculates the functional similarity o...
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Veröffentlicht in: | Computational biology and chemistry 2024-06, Vol.110, p.108090-108090, Article 108090 |
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creator | Baruah, Bikash Dutta, Manash P. Banerjee, Subhasish Bhattacharyya, Dhruba K. |
description | The development of functionally enriched and biologically competent biclustering algorithm is essential for extracting hidden information from massive biological datasets. This paper presents a novel biclustering ensemble called EnsemBic based on p-value, which calculates the functional similarity of genetic associations. To validate the effectiveness and robustness of EnsemBic, we apply three well-known biclustering techniques, viz. Laplace Prior, iBBiG, and xMotif to implement EnsemBic and have been compared using different leading parameters. It is observed that the EnsemBic outperforms its competing algorithms in several prominent functional and biological measures. Next, the biclusters obtained from EnsemBic are used to identify potential biomarkers of Esophageal Squamous Cell Carcinoma (ESCC) by exploring topological and biological relevance with reference to the elite genes, attained from genecards. Finally, we discover that the genes F2RL3, APPL1, CALM1, IFNGR1, LPAR1, ANGPT2, ARPC2, CGN, CLDN7, ATP6V1C2, CEACAM1, FTL, PLAU,PSMB4, and EPHB2 carry both the topological and biological significance of previously established ESCC elite genes. Therefore, we declare the aforementioned genes as potential biomarkers of ESCC.
[Display omitted]
•EnsemBic exclusively comprises the high-quality biclusters obtained from the well-known base biclustering algorithms.•The biclusters extracted by EnsemBic undergo validation using prominent functional and biological measures, establishing their high functional and biological significance.•Topological analysis reveals the usefulness of extracted biclusters in identifying potential biomarkers of ESCC.•Validation through gene enrichment analysis and pathway exploration ensures the biological relevance and significance of the identified potential biomarkers.•A specialized literature survey substantiates the significance of the identified potential biomarkers responsible for ESCC. |
doi_str_mv | 10.1016/j.compbiolchem.2024.108090 |
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[Display omitted]
•EnsemBic exclusively comprises the high-quality biclusters obtained from the well-known base biclustering algorithms.•The biclusters extracted by EnsemBic undergo validation using prominent functional and biological measures, establishing their high functional and biological significance.•Topological analysis reveals the usefulness of extracted biclusters in identifying potential biomarkers of ESCC.•Validation through gene enrichment analysis and pathway exploration ensures the biological relevance and significance of the identified potential biomarkers.•A specialized literature survey substantiates the significance of the identified potential biomarkers responsible for ESCC.</description><identifier>ISSN: 1476-9271</identifier><identifier>EISSN: 1476-928X</identifier><identifier>DOI: 10.1016/j.compbiolchem.2024.108090</identifier><identifier>PMID: 38759483</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Algorithms ; Biclustering algorithm ; Biological analysis ; Biomarkers, Tumor - genetics ; Cluster Analysis ; Elite gene ; Esophageal Neoplasms - genetics ; Esophageal Squamous Cell Carcinoma - genetics ; FuncAssociate ; Humans ; Potential biomarker ; Topological analysis</subject><ispartof>Computational biology and chemistry, 2024-06, Vol.110, p.108090-108090, Article 108090</ispartof><rights>2024 Elsevier Ltd</rights><rights>Copyright © 2024 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c323t-a04c215d5199e4bc8bcdd98a5cb23f1dcebc4a88b943241268cbab9cd3dde8823</cites><orcidid>0000-0001-7485-6897</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.compbiolchem.2024.108090$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38759483$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Baruah, Bikash</creatorcontrib><creatorcontrib>Dutta, Manash P.</creatorcontrib><creatorcontrib>Banerjee, Subhasish</creatorcontrib><creatorcontrib>Bhattacharyya, Dhruba K.</creatorcontrib><title>EnsemBic: An effective ensemble of biclustering to identify potential biomarkers of esophageal squamous cell carcinoma</title><title>Computational biology and chemistry</title><addtitle>Comput Biol Chem</addtitle><description>The development of functionally enriched and biologically competent biclustering algorithm is essential for extracting hidden information from massive biological datasets. This paper presents a novel biclustering ensemble called EnsemBic based on p-value, which calculates the functional similarity of genetic associations. To validate the effectiveness and robustness of EnsemBic, we apply three well-known biclustering techniques, viz. Laplace Prior, iBBiG, and xMotif to implement EnsemBic and have been compared using different leading parameters. It is observed that the EnsemBic outperforms its competing algorithms in several prominent functional and biological measures. Next, the biclusters obtained from EnsemBic are used to identify potential biomarkers of Esophageal Squamous Cell Carcinoma (ESCC) by exploring topological and biological relevance with reference to the elite genes, attained from genecards. Finally, we discover that the genes F2RL3, APPL1, CALM1, IFNGR1, LPAR1, ANGPT2, ARPC2, CGN, CLDN7, ATP6V1C2, CEACAM1, FTL, PLAU,PSMB4, and EPHB2 carry both the topological and biological significance of previously established ESCC elite genes. Therefore, we declare the aforementioned genes as potential biomarkers of ESCC.
[Display omitted]
•EnsemBic exclusively comprises the high-quality biclusters obtained from the well-known base biclustering algorithms.•The biclusters extracted by EnsemBic undergo validation using prominent functional and biological measures, establishing their high functional and biological significance.•Topological analysis reveals the usefulness of extracted biclusters in identifying potential biomarkers of ESCC.•Validation through gene enrichment analysis and pathway exploration ensures the biological relevance and significance of the identified potential biomarkers.•A specialized literature survey substantiates the significance of the identified potential biomarkers responsible for ESCC.</description><subject>Algorithms</subject><subject>Biclustering algorithm</subject><subject>Biological analysis</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Cluster Analysis</subject><subject>Elite gene</subject><subject>Esophageal Neoplasms - genetics</subject><subject>Esophageal Squamous Cell Carcinoma - genetics</subject><subject>FuncAssociate</subject><subject>Humans</subject><subject>Potential biomarker</subject><subject>Topological analysis</subject><issn>1476-9271</issn><issn>1476-928X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkMlu2zAQQImiQeOk_YWC6KkXO1wkmcrNTbMBAXJJgd4Icjiy6UqiQkoG8vel4DToMScOOG-2R8g3zlac8epiv4LQDdaHFnbYrQQTRU4oVrMPZMGLdbWshfr98S1e81NyltKeMSEZKz-RU6nWZV0ouSCH6z5h98PDJd30FJsGYfQHpDh_2xZpaKj10E5pxOj7LR0D9Q770TcvdAjjHJk2I6Ez8Q_GNBdgCsPObDEn0vNkujAlCti2FEwE32f0MzlpTJvwy-t7Tn7dXD9d3S0fHm_vrzYPS5BCjkvDChC8dCWvaywsKAvO1cqUYIVsuAO0UBilbF1IUXBRKbDG1uCkc6iUkOfk-7HvEMPzhGnUnU_zKqbHvJWWrKyqaq2kzOjlEYUYUorY6CH6fNSL5kzP3vVe_-9dz9710Xsu_vo6Z7IdurfSf6Iz8PMIYL724DHqBB57QOdjVq5d8O-Z8xcSQJ5j</recordid><startdate>202406</startdate><enddate>202406</enddate><creator>Baruah, Bikash</creator><creator>Dutta, Manash P.</creator><creator>Banerjee, Subhasish</creator><creator>Bhattacharyya, Dhruba K.</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-7485-6897</orcidid></search><sort><creationdate>202406</creationdate><title>EnsemBic: An effective ensemble of biclustering to identify potential biomarkers of esophageal squamous cell carcinoma</title><author>Baruah, Bikash ; Dutta, Manash P. ; Banerjee, Subhasish ; Bhattacharyya, Dhruba K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c323t-a04c215d5199e4bc8bcdd98a5cb23f1dcebc4a88b943241268cbab9cd3dde8823</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Biclustering algorithm</topic><topic>Biological analysis</topic><topic>Biomarkers, Tumor - genetics</topic><topic>Cluster Analysis</topic><topic>Elite gene</topic><topic>Esophageal Neoplasms - genetics</topic><topic>Esophageal Squamous Cell Carcinoma - genetics</topic><topic>FuncAssociate</topic><topic>Humans</topic><topic>Potential biomarker</topic><topic>Topological analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Baruah, Bikash</creatorcontrib><creatorcontrib>Dutta, Manash P.</creatorcontrib><creatorcontrib>Banerjee, Subhasish</creatorcontrib><creatorcontrib>Bhattacharyya, Dhruba K.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Computational biology and chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Baruah, Bikash</au><au>Dutta, Manash P.</au><au>Banerjee, Subhasish</au><au>Bhattacharyya, Dhruba K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>EnsemBic: An effective ensemble of biclustering to identify potential biomarkers of esophageal squamous cell carcinoma</atitle><jtitle>Computational biology and chemistry</jtitle><addtitle>Comput Biol Chem</addtitle><date>2024-06</date><risdate>2024</risdate><volume>110</volume><spage>108090</spage><epage>108090</epage><pages>108090-108090</pages><artnum>108090</artnum><issn>1476-9271</issn><eissn>1476-928X</eissn><abstract>The development of functionally enriched and biologically competent biclustering algorithm is essential for extracting hidden information from massive biological datasets. This paper presents a novel biclustering ensemble called EnsemBic based on p-value, which calculates the functional similarity of genetic associations. To validate the effectiveness and robustness of EnsemBic, we apply three well-known biclustering techniques, viz. Laplace Prior, iBBiG, and xMotif to implement EnsemBic and have been compared using different leading parameters. It is observed that the EnsemBic outperforms its competing algorithms in several prominent functional and biological measures. Next, the biclusters obtained from EnsemBic are used to identify potential biomarkers of Esophageal Squamous Cell Carcinoma (ESCC) by exploring topological and biological relevance with reference to the elite genes, attained from genecards. Finally, we discover that the genes F2RL3, APPL1, CALM1, IFNGR1, LPAR1, ANGPT2, ARPC2, CGN, CLDN7, ATP6V1C2, CEACAM1, FTL, PLAU,PSMB4, and EPHB2 carry both the topological and biological significance of previously established ESCC elite genes. Therefore, we declare the aforementioned genes as potential biomarkers of ESCC.
[Display omitted]
•EnsemBic exclusively comprises the high-quality biclusters obtained from the well-known base biclustering algorithms.•The biclusters extracted by EnsemBic undergo validation using prominent functional and biological measures, establishing their high functional and biological significance.•Topological analysis reveals the usefulness of extracted biclusters in identifying potential biomarkers of ESCC.•Validation through gene enrichment analysis and pathway exploration ensures the biological relevance and significance of the identified potential biomarkers.•A specialized literature survey substantiates the significance of the identified potential biomarkers responsible for ESCC.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>38759483</pmid><doi>10.1016/j.compbiolchem.2024.108090</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-7485-6897</orcidid></addata></record> |
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subjects | Algorithms Biclustering algorithm Biological analysis Biomarkers, Tumor - genetics Cluster Analysis Elite gene Esophageal Neoplasms - genetics Esophageal Squamous Cell Carcinoma - genetics FuncAssociate Humans Potential biomarker Topological analysis |
title | EnsemBic: An effective ensemble of biclustering to identify potential biomarkers of esophageal squamous cell carcinoma |
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