Splicing sites prediction of human genome using machine learning techniques
The accurate splice site prediction has several applications in the field of medical sciences and biochemistry. For instance, any mutation affecting the splice site will lead to genetic diseases and cancer such as Lynch syndrome and breast cancer. For this purpose, collecting the Ribonucleic Acid (R...
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description | The accurate splice site prediction has several applications in the field of medical sciences and biochemistry. For instance, any mutation affecting the splice site will lead to genetic diseases and cancer such as Lynch syndrome and breast cancer. For this purpose, collecting the Ribonucleic Acid (RNA) samples is an efficient and convenient method to detect the involvement of splicing defects in disease formation. Therefore, the present study aims to develop an accurate and robust Computer-Aided Diagnosis (CAD) method for swift and precise targeting of splice site sequences. A composite features-based model is proposed by integrating three different sample representation methods i.e., Dinucleotide Composition (DNC), Trinucleotide Composition (TNC) and Tetranucleotide Composition (TetraNC) for precise splice site prediction after converting the DNA sequences into numerical descriptors. The precision and accuracy of these features are analyzed by applying different machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB). Results show that the proposed model of composite features vector with SVM classifier achieved an accuracy of 95.20% and 97.50% for donor and acceptor sites datasets, respectively. |
doi_str_mv | 10.1007/s11042-021-10619-3 |
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For instance, any mutation affecting the splice site will lead to genetic diseases and cancer such as Lynch syndrome and breast cancer. For this purpose, collecting the Ribonucleic Acid (RNA) samples is an efficient and convenient method to detect the involvement of splicing defects in disease formation. Therefore, the present study aims to develop an accurate and robust Computer-Aided Diagnosis (CAD) method for swift and precise targeting of splice site sequences. A composite features-based model is proposed by integrating three different sample representation methods i.e., Dinucleotide Composition (DNC), Trinucleotide Composition (TNC) and Tetranucleotide Composition (TetraNC) for precise splice site prediction after converting the DNA sequences into numerical descriptors. The precision and accuracy of these features are analyzed by applying different machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB). Results show that the proposed model of composite features vector with SVM classifier achieved an accuracy of 95.20% and 97.50% for donor and acceptor sites datasets, respectively.</description><identifier>ISSN: 1380-7501</identifier><identifier>EISSN: 1573-7721</identifier><identifier>DOI: 10.1007/s11042-021-10619-3</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>1155T: Advanced machine learning algorithms for biomedical data and imaging ; Accuracy ; Algorithms ; Breast cancer ; Composition ; Computer Communication Networks ; Computer Science ; Data Structures and Information Theory ; Deoxyribonucleic acid ; DNA ; Gene sequencing ; Machine learning ; Multimedia Information Systems ; Mutation ; Ribonucleic acid ; RNA ; Robustness (mathematics) ; Special Purpose and Application-Based Systems ; Splicing ; Support vector machines</subject><ispartof>Multimedia tools and applications, 2021-08, Vol.80 (20), p.30439-30460</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-df518c3b0c428c5c19098c69a2d438bb260dd0d446c8b0fb13744d3572b42ea43</citedby><cites>FETCH-LOGICAL-c319t-df518c3b0c428c5c19098c69a2d438bb260dd0d446c8b0fb13744d3572b42ea43</cites><orcidid>0000-0003-4055-7412</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11042-021-10619-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11042-021-10619-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Ullah, Waseem</creatorcontrib><creatorcontrib>Muhammad, Khan</creatorcontrib><creatorcontrib>Ul Haq, Ijaz</creatorcontrib><creatorcontrib>Ullah, Amin</creatorcontrib><creatorcontrib>Ullah Khattak, Saeed</creatorcontrib><creatorcontrib>Sajjad, Muhammad</creatorcontrib><title>Splicing sites prediction of human genome using machine learning techniques</title><title>Multimedia tools and applications</title><addtitle>Multimed Tools Appl</addtitle><description>The accurate splice site prediction has several applications in the field of medical sciences and biochemistry. For instance, any mutation affecting the splice site will lead to genetic diseases and cancer such as Lynch syndrome and breast cancer. For this purpose, collecting the Ribonucleic Acid (RNA) samples is an efficient and convenient method to detect the involvement of splicing defects in disease formation. Therefore, the present study aims to develop an accurate and robust Computer-Aided Diagnosis (CAD) method for swift and precise targeting of splice site sequences. A composite features-based model is proposed by integrating three different sample representation methods i.e., Dinucleotide Composition (DNC), Trinucleotide Composition (TNC) and Tetranucleotide Composition (TetraNC) for precise splice site prediction after converting the DNA sequences into numerical descriptors. The precision and accuracy of these features are analyzed by applying different machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB). 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For instance, any mutation affecting the splice site will lead to genetic diseases and cancer such as Lynch syndrome and breast cancer. For this purpose, collecting the Ribonucleic Acid (RNA) samples is an efficient and convenient method to detect the involvement of splicing defects in disease formation. Therefore, the present study aims to develop an accurate and robust Computer-Aided Diagnosis (CAD) method for swift and precise targeting of splice site sequences. A composite features-based model is proposed by integrating three different sample representation methods i.e., Dinucleotide Composition (DNC), Trinucleotide Composition (TNC) and Tetranucleotide Composition (TetraNC) for precise splice site prediction after converting the DNA sequences into numerical descriptors. The precision and accuracy of these features are analyzed by applying different machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB). 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subjects | 1155T: Advanced machine learning algorithms for biomedical data and imaging Accuracy Algorithms Breast cancer Composition Computer Communication Networks Computer Science Data Structures and Information Theory Deoxyribonucleic acid DNA Gene sequencing Machine learning Multimedia Information Systems Mutation Ribonucleic acid RNA Robustness (mathematics) Special Purpose and Application-Based Systems Splicing Support vector machines |
title | Splicing sites prediction of human genome using machine learning techniques |
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