Using artificial intelligence techniques for COVID-19 genome analysis

Abstract

The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province, China. COVID-19 genome sequencing is critical to understanding the virus behavior, its origin, how fast it mutates, and for the development of drugs/vaccines and effective preventive strategies. This paper investigates the use of artificial intelligence techniques to learn interesting information from COVID-19 genome sequences. Sequential pattern mining (SPM) is first applied on a computer-understandable corpus of COVID-19 genome sequences to see if interesting hidden patterns can be found, which reveal frequent patterns of nucleotide bases and their relationships with each other. Second, sequence prediction models are applied to the corpus to evaluate if nucleotide base(s) can be predicted from previous ones. Third, for mutation analysis in genome sequences, an algorithm is designed to find the locations in the genome sequences where the nucleotide bases are changed and to calculate the mutation rate. Obtained results suggest that SPM and mutation analysis techniques can reveal interesting information and patterns in COVID-19 genome sequences to examine the evolution and variations in COVID-19 strains respectively.

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

“…The gene coding region, also recognized as the CDS (coding sequence), is the part of a gene's DNA or RNA that is coded for protein. 5 To find the coding regions CDS in the Fig. 5 Composition of amino acids Fig.…”

“…Even though it is usually a tedious and resource-intensive procedure that mostly depends on field expertise. Various strains of the initial genome sequencing of COVID-19 did not set timely actionable discernments, yet many features of disease behavior are unknown [5].…”

“…Ahmed et al [13,33] used a surveillance data set with deep learning models to monitor social distance and control infection transmission of COVID-19. Some researchers, e.g., [5] also used genome sequence data sets with artificial intelligence based method e.g., sequential pattern mining for COVID-19 genome analysis.…”

An integrated framework for COVID‐19 classification based on classical and quantum transfer learning from a chest radiograph

“… 54 Proposed a genome analysis of COVID‐19 by using the AI. 58 Both chest x‐ray images and CT scan based automatic detection of corona virus is presented in References 59 , 60 , 61 . For analysis and detection of coronavirus a DL model is presented by Kedia et al 62 In the field AI and ML, TL is the most important and extensively used methodology that works based on previously trained ML models and re‐use the relevant knowledge of these trained models to different tasks.…”

Cross-sectional analysis and data-driven forecasting of confirmed COVID-19 cases

“…In a recent study, confirmed COVID-19 cases of a few European countries, e.g., Denmark, Belgium, Germany, etc., were modeled and estimated by ARIMA, NARNN (Nonlinear Autoregression Neural Network) and LSTM (Long-Short Term Memory) approaches, respectively [ 26 ]. Besides, artificial intelligence techniques are used to learn interesting information and patterns from COVID-19 genome sequences [ 27 ]. As the COVID-19 disease spreads worldwide and the regional resilience requirement rises, daily-level regional data are needed for a detailed analysis and forecasting, e.g., population mobility, smoking rate, and the number of hospitals.…”