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Gene Ontology Semi-supervised Clustering for Prediction of Genes Functions

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dc.contributor.advisor Shill, Dr. Pintu Chandra
dc.contributor.author Paul, Animesh Kumar
dc.date.accessioned 2018-05-19T11:49:08Z
dc.date.available 2018-05-19T11:49:08Z
dc.date.issued 2018-02
dc.identifier.other ID 1607507
dc.identifier.uri http://hdl.handle.net/20.500.12228/113
dc.description This thesis is submitted to the Department of Computer Science and Engineering, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, February, 2018. en_US
dc.description Cataloged from PDF Version of Thesis.
dc.description Includes bibliographical references (pages 30-36).
dc.description.abstract Different products of gene expression work together in a cell for each living organism to achieve different biological processes. Many proteins play different roles depending on the environment of the organism for the functioning of a cell. Usually, most conventional methods are not able to analyze the functions of the genes biologically. In this thesis, we propose a gene ontology (GO) annotation based semi-supervised clustering algorithm called GO Fuzzy relational clustering (GO-FRC). In GO-FRC, one gene is allowed to be assigned to multiple clusters, and that is biologically relevant to the behavior of gene. In the clustering process, GO-FRC utilizes the useful biological knowledge, which is available in the form of a Gene Ontology, as a prior knowledge along with the gene expression data. The prior knowledge helps to improve the coherence of the groups concerning the knowledge field. The proposed GO-FRC has been tested on the two yeast (Saccharomyces cerevisiae) expression profiles datasets (Eisen and Dream 5 yeast datasets) and has compared with other state-of-the-art clustering algorithms. Experimental results imply that GO-FRC can produce more biologically relevant clusters with the use of the small amount of GO annotations. en_US
dc.description.statementofresponsibility Animesh Kumar Paul
dc.format.extent 43 pages
dc.language.iso en_US en_US
dc.publisher Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh. en_US
dc.rights Khulna University of Engineering & Technology (KUET) thesis/dissertation/internship reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subject Gene Ontology en_US
dc.subject Clustering
dc.subject Gene
dc.title Gene Ontology Semi-supervised Clustering for Prediction of Genes Functions en_US
dc.type Thesis en_US
dc.description.degree Master of Science in Computer Science and Engineering
dc.contributor.department Department of Computer Science and Engineering


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