Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses

Imran Ahmed, Gwanggil Jeon

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

Abstract

Recent pandemic of COVID-19 (Coronavirus) caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) has been growing lethally with unusual speed. It has infected millions of people and continues a mortifying influence on the global population’s health and well-being. In this situation, genome sequence analysis and advanced artificial intelligence techniques may help researchers and medical experts to understand the genetic variants of COVID-19 or SARS-CoV-2. Genome sequence analysis of COVID-19 is crucial to understand the virus’s origin, behavior, and structure, which might help produce/develop vaccines, antiviral drugs, and efficient preventive strategies. This paper introduces an artificial intelligence based system to perform genome sequence analysis of COVID-19 and alike viruses, e.g., SARS, middle east respiratory syndrome, and Ebola. The system helps to get important information from the genome sequences of different viruses. We perform comparative data analysis by extracting basic information of COVID-19 and other genome sequences, including information of nucleotides composition and their frequency, tri-nucleotide compositions, count of amino acids, alignment between genome sequences, and their DNA similarity information. We use different visualization methods to analyze these viruses’ genome sequences and, finally, apply machine learning based classifier support vector machine to classify different genome sequences. The data set of different virus genome sequences are obtained from an online publicly accessible data center repository. The system achieves good classification results with an accuracy of 97% for COVID-19, 96%, SARS, and 95% for MERS and Ebola genome sequences, respectively. Graphic Abstract: [Figure not available: see fulltext.]

Original languageEnglish
Pages (from-to)504-519
Number of pages16
JournalInterdisciplinary sciences, computational life sciences
Volume14
Issue number2
DOIs
Publication statusPublished - Jun 2022
Externally publishedYes

Keywords

  • Artificial intelligence
  • COVID-19
  • Genome sequence analysis
  • Machine learning
  • SVM

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