Detection of COVID-19 Using Protein Sequence Data via Machine Learning Classification Approach
The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in late 2019 resulted in the COVID-19 pandemic, necessitating rapid and accurate detection of pathogens through protein sequence data. This study is aimed at developing an efficient classification model for coronavirus pro...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2023/9991095 |
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| _version_ | 1832547356191490048 |
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| author | Siti Aminah Gianinna Ardaneswari Mufarrido Husnah Ghani Deori Handi Bagus Prasetyo |
| author_facet | Siti Aminah Gianinna Ardaneswari Mufarrido Husnah Ghani Deori Handi Bagus Prasetyo |
| author_sort | Siti Aminah |
| collection | DOAJ |
| description | The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in late 2019 resulted in the COVID-19 pandemic, necessitating rapid and accurate detection of pathogens through protein sequence data. This study is aimed at developing an efficient classification model for coronavirus protein sequences using machine learning algorithms and feature selection techniques to aid in the early detection and prediction of novel viruses. We utilized a dataset comprising 2000 protein sequences, including 1000 SARS-CoV-2 sequences and 1000 non-SARS-CoV-2 sequences. Feature extraction provided 27 essential features representing the primary structural data, achieved through the Discere package. To optimize performance, we employed machine learning classification algorithms such as K-nearest neighbor (KNN), XGBoost, and Naïve Bayes, along with feature selection techniques like genetic algorithm (GA), LASSO, and support vector machine recursive feature elimination (SVM-RFE). The SVM-RFE+KNN model exhibited exceptional performance, achieving a classification accuracy of 99.30%, specificity of 99.52%, and sensitivity of 99.55%. These results demonstrate the model’s efficacy in accurately classifying coronavirus protein sequences. Our research successfully developed a robust classification model capable of early detection and prediction of protein sequences in SARS-CoV-2 and other coronaviruses. This advancement holds great promise in facilitating the development of targeted treatments and preventive strategies for combating future viral outbreaks. |
| format | Article |
| id | doaj-art-4f8435e234e948d7ba6efc6fb0d1b9c4 |
| institution | Kabale University |
| issn | 1687-0042 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-4f8435e234e948d7ba6efc6fb0d1b9c42025-02-03T06:45:14ZengWileyJournal of Applied Mathematics1687-00422023-01-01202310.1155/2023/9991095Detection of COVID-19 Using Protein Sequence Data via Machine Learning Classification ApproachSiti Aminah0Gianinna Ardaneswari1Mufarrido Husnah2Ghani Deori3Handi Bagus Prasetyo4Department of MathematicsDepartment of MathematicsDepartment of MathematicsDepartment of MathematicsDepartment of MathematicsThe emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in late 2019 resulted in the COVID-19 pandemic, necessitating rapid and accurate detection of pathogens through protein sequence data. This study is aimed at developing an efficient classification model for coronavirus protein sequences using machine learning algorithms and feature selection techniques to aid in the early detection and prediction of novel viruses. We utilized a dataset comprising 2000 protein sequences, including 1000 SARS-CoV-2 sequences and 1000 non-SARS-CoV-2 sequences. Feature extraction provided 27 essential features representing the primary structural data, achieved through the Discere package. To optimize performance, we employed machine learning classification algorithms such as K-nearest neighbor (KNN), XGBoost, and Naïve Bayes, along with feature selection techniques like genetic algorithm (GA), LASSO, and support vector machine recursive feature elimination (SVM-RFE). The SVM-RFE+KNN model exhibited exceptional performance, achieving a classification accuracy of 99.30%, specificity of 99.52%, and sensitivity of 99.55%. These results demonstrate the model’s efficacy in accurately classifying coronavirus protein sequences. Our research successfully developed a robust classification model capable of early detection and prediction of protein sequences in SARS-CoV-2 and other coronaviruses. This advancement holds great promise in facilitating the development of targeted treatments and preventive strategies for combating future viral outbreaks.http://dx.doi.org/10.1155/2023/9991095 |
| spellingShingle | Siti Aminah Gianinna Ardaneswari Mufarrido Husnah Ghani Deori Handi Bagus Prasetyo Detection of COVID-19 Using Protein Sequence Data via Machine Learning Classification Approach Journal of Applied Mathematics |
| title | Detection of COVID-19 Using Protein Sequence Data via Machine Learning Classification Approach |
| title_full | Detection of COVID-19 Using Protein Sequence Data via Machine Learning Classification Approach |
| title_fullStr | Detection of COVID-19 Using Protein Sequence Data via Machine Learning Classification Approach |
| title_full_unstemmed | Detection of COVID-19 Using Protein Sequence Data via Machine Learning Classification Approach |
| title_short | Detection of COVID-19 Using Protein Sequence Data via Machine Learning Classification Approach |
| title_sort | detection of covid 19 using protein sequence data via machine learning classification approach |
| url | http://dx.doi.org/10.1155/2023/9991095 |
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