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Identification of Genetic Promoter through Stochastic Approach

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dc.contributor.author Ghyas, Qazi Adnan
dc.date.accessioned 2023-08-24T05:19:31Z
dc.date.available 2023-08-24T05:19:31Z
dc.date.issued 2007-12-06
dc.identifier.uri http://dspace.ewubd.edu:8080/handle/123456789/4093
dc.description This thesis submitted in partial fulfillment of the requirements for the degree of Masters of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh en_US
dc.description.abstract Analysis of a gene sequence, which is transcribed into RNA and then translated inti protein, is a difficult task. If this could be achieved, it would make possible better understand how the organisms are developed from DNA information. The behavior of gene is highly influenced by promoter sequences residing up stream or downstream of the Transcription Start Site (TSS). The promoter recognition pro, access is a part of the complex process where genes interact with each other over time and actually regulates the whole working process of a cell. This paper attempts to develop an efficient algorithm that can successfully distinguish promoters and non promoters by analyzing statistical data. A learning model is developed from the known dataset to predict the unknown ones. Results: We have developed an efficient algorithm that can successfully distinguish genes from non-gene sequences by analyzing statistical data. A learning model is initially developed to train the Support Vector Machine (SVM) to identify distinctive features between gene and non gene. Then this context was used to predict other foreign sequence by the SVM. Our system has been tested using standard plant prom data sequence from the EMBL and the performances are: 0.86 for the Sensitivity and 0.90 for the specificity. Identification en_US
dc.language.iso en_US en_US
dc.publisher East West University en_US
dc.relation.ispartofseries ;CSE00004(2)
dc.subject RNA and Translated protein, Transcription Start Site en_US
dc.title Identification of Genetic Promoter through Stochastic Approach en_US
dc.type Thesis en_US


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