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dc.contributor.advisorShowkat, Dilruba
dc.contributor.authorObayed Bin Mahfuz
dc.date.accessioned2015-09-03T06:30:34Z
dc.date.available2015-09-03T06:30:34Z
dc.date.copyright2015
dc.date.issued2015-08
dc.identifier.otherID 12101103
dc.identifier.urihttp://hdl.handle.net/10361/4371
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2015.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 51-53).
dc.description.abstractGene Regulatory Networks are the basic functional unit in living organisms. Gene Regulatory Network mainly refers to the behavior of thousands of genes (inside the chromosome of cell) with other genes. Each gene has expression levels and interaction with other genes. These interactions and expression levels can be calculated from their amount and time duration of protein production. Due to the invention of DNA microarray in biotechnology, we are able to find gene expression levels from real genetic regulatory networks. Now it’s the time to find a reverse process to reach a satisfactory result that matches with those data derived from DNA microarray. If we can find those values that satisfy the result, then we can predict our gene behavior much early. Watching abnormal gene behavior, diseases can be found .Thus it can be a revolutionary step towards medicine and diagnosis sector. If we reach better accuracy then it will also help us to develop tissue and organs. That means, for chronic disease or any other problem if one’s heart cannot pump blood, then he can repair his heart by making a new heart developed from the muscle cells from any other organ of his body. Biological systems are very much complex in nature. And S-system model is a recent and popular class to model biological systems. Hence, I am using S-system class for modeling. Gene Regulatory Networks contain a large number of genes. And artificial bee colony is best suited for population based problems. Hence, I am proposing an inference algorithm of gene regulatory network on the framework of S-system class of ordinary differential equations (ODEs) and artificial bee colony algorithm.en_US
dc.description.statementofresponsibilityObayed Bin Mahfuz
dc.format.extent53 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis 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.subjectComputer science and engineeringen_US
dc.subjectGene regulatory networken_US
dc.subjectInferenceen_US
dc.subjectS-systemen_US
dc.subjectDNA microarrayen_US
dc.subjectArtificial bee colonyen_US
dc.titleInference of gene regulatory network with S-system and artificial Bee Colony algorithmen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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