Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms.
The rapid spread of coronavirus disease (COVID-19) has become a worldwide pandemic and affected more than 15 million patients reported in 27 countries. Therefore, the computational biology carrying this virus that correlates with the human population urgently needs to be understood. In this paper, t...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 9; pp. 1723 - 1735 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152044049&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152044049 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2021 vid: 59 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152044049 151501880 152044049 NLM34291385 10.1007/s11517-021-02412-z NLM34291385 152044049 ppf: 1723 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms. aug: au: Afify, Heba M. Zanaty, Muhammad S. affil: Systems and Biomedical Engineering Department, Higher Institute of Engineering in El-Shorouk City, Cairo, Egypt sug: ab: The rapid spread of coronavirus disease (COVID-19) has become a worldwide pandemic and affected more than 15 million patients reported in 27 countries. Therefore, the computational biology carrying this virus that correlates with the human population urgently needs to be understood. In this paper, the classification of the human protein sequences of COVID-19, according to the country, is presented based on machine learning algorithms. The proposed model is based on distinguishing 9238 sequences using three stages, including data preprocessing, data labeling, and classification. In the first stage, data preprocessing's function converts the amino acids of COVID-19 protein sequences into eight groups of numbers based on the amino acids' volume and dipole. It is based on the conjoint triad (CT) method. In the second stage, there are two methods for labeling data from 27 countries from 0 to 26. The first method is based on selecting one number for each country according to the code numbers of countries, while the second method is based on binary elements for each country. According to their countries, machine learning algorithms are used to discover different COVID-19 protein sequences in the last stage. The obtained results demonstrate 100% accuracy, 100% sensitivity, and 90% specificity via the country-based binary labeling method with a linear support vector machine (SVM) classifier. Furthermore, with significant infection data, the USA is more prone to correct classification compared to other countries with fewer data. The unbalanced data for COVID-19 protein sequences is considered a major issue, especially as the US's available data represents 76% of a total of 9238 sequences. The proposed model will act as a prediction tool for the COVID-19 protein sequences in different countries. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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