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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Veröffentlicht in:Multimedia tools and applications 2021-08, Vol.80 (20), p.30439-30460
Hauptverfasser: Ullah, Waseem, Muhammad, Khan, Ul Haq, Ijaz, Ullah, Amin, Ullah Khattak, Saeed, Sajjad, Muhammad
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container_issue 20
container_start_page 30439
container_title Multimedia tools and applications
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creator Ullah, Waseem
Muhammad, Khan
Ul Haq, Ijaz
Ullah, Amin
Ullah Khattak, Saeed
Sajjad, Muhammad
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). 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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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