The Potentials of Deep Learning Techniques for the Classification of SARS-CoV-2 Variants Based on Genomic Sequence Information

Genetic mutations give rise to a quasispecies of drug/vaccine-resistant and virulent organisms. These organisms are classified as strains or variants depending on the extent of their phenotypic manifestation. Thus, there is a thin dichotomy between SARS-CoV-2 strains and their associated variants. This paper sought to comprehensively review the successes achieved in the classification of SARS-CoV-2 strains based on genomic sequences (GSs) using deep learning architectures, thereby stimulating further research on the variants identified recently. Selective screening and analysis of research articles centered on deep learning architectures employed for SARS-CoV-2 detection based on GS information were carried out. This incorporated the use of relevant key/search terms and logical/Boolean operators to scan through the Scopus repository. To provide a foundation for future investigations on the classification of SARS-CoV-2 strains, meticulous analysis of the three key aspects, such as , methodology, and conclusion, was implemented. Despite the high level of intra-species similarity, this article presents new studies that use deep learning technology to detect SARS-CoV-2 strains on the premise of the primary sequence of nucleotides in their genome. Manually searching through specific genes for mutations to identify variants after sequencing can be very laborious. This is where the use of computational acumen comes into play. Deep learning, an offshoot of machine learning, has been utilized in various literature to tackle such problems. Rapid identification of SARS-CoV-2 variant after sequencing aids quick response by clinicians to administer relevant drugs and save lives. Also, governments utilize this information to map out strategies for the timely containment of the spread of an identified variant with elevated virulence. The deep learning models reported in this paper show the remarkable predictive results achieved in identifying SARS-CoV-2 strains. However, no work has been done on the identification of recent variants reported globally. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detection of COVID-19 from Chest X-Ray Images using CNN and ANN Approach

The occurrence of coronavirus (COVID-19), which causes respiratory illnesses, is higher than in 2003. (SARS). COVID-19 and SARS are both spreading over regions and infecting living beings, with more than 73,435 deaths and more than 2000 deaths documented as of August 12, 2020. In contrast, SARS killed 774 lives in 2003, whereas COVID-19 claimed more in the shortest amount of time. However, the fundamental difference between them is that, after 17 years of SARS, a powerful new tool has developed that could be utilized to combat the virus and keep it within reasonable boundaries. One of these tools is machine learning (ML). Recently, machine learning (ML) has caused a paradigm shift in the healthcare industry, and its use in the COVID-19 outbreak could be profitable, especially in forecasting the location of the next outbreak. The use of AI in COVID-19 diagnosis and monitoring can be accelerated, reducing the time and cost of these processes. As a result, this study uses ANN and CNN techniques to detect COVID-19 from chest x-ray pictures, with 95% and 75% accuracy, respectively. Machine learning has greatly enhanced monitoring, diagnosis, monitoring, analysis, forecasting, touch tracking, and medication/vaccine production processes for the Covid-19 disease outbreak, reducing human involvement in nursing treatment. © 2022. International Journal of Advanced Computer Science and Applications.All Rights Reserved.

Binding site identification of COVID-19 main protease 3D structure by homology modeling

The influx of coronavirus in 2019 (COVID-19) has recorded millions of infection cases with several deaths worldwide. There is no effective treatment, but recent studies have shown that its enzymes maybe considered as potential drug target. The purpose of this work was to identify the binding site in-silico and present the 3D structure of COVID-19 main-protease (Mpro) by homology modeling through multiple alignment followed by optimization and validation. The modeling was done by Swiss-Model template library. The obtained homotrimer oligo-state model was verified for reliability using PROCHECK, Verify3D, MolProbity and QMEAN. HHBlits software was used to determine structures that matched the target sequence by evolution. Structure quality verification through Ramachandran plot showed an abundance of 99.3% of amino acid residues in allowed regions while 0.1% in disallowed region. The Verify3D rated the structure a 90.87% PASS of residues having an average 3D-1D score of at least 0.2, which validates a good environment profile for the Mpro model. The features of the secondary structure indicated that the structure contains 32.05% α-helix and 37.17% random coil with 25.92 extended strand. The result of this study suggests that blocking expression of this protein may constitute an efficient approach for infection transmission blockage. © 2021 Institute of Advanced Engineering and Science. All rights reserved.

Dimensionality Reduction of Social Media Application Attributes for Ubiquitous Learning Using Principal Component Analysis

Ubiquitous learning is anywhere and anytime learning using e-learning and m-learning platforms. Learning takes place regularly on mobile devices. School-based instructors and learners have capitalised on ubiquitous learning platforms in unprecedented times such as COVID-19. There has been a proliferation of social media applications for ubiquitous learning. There are a vast number of attributes of the social media applications that must be considered for it to be deemed suitable for education. Further to this, mobile and desktop accessibility criteria must be considered. The aim of this research study was to determine the high impacting and most pertinent criteria to evaluate social media applications for school-based ubiquitous learning. Data was collected from 30 experts in the field of teaching and learning who were asked to evaluate 60 criteria. Principal Component Analysis (PCA) was the method employed for the dimensionality reduction. PCA was implemented using singular value decomposition (SVD) on R-Studio. The results showed loading values from principal component one for the top 40 educational requirements and technology criteria of the 60 criteria used in the study. The implications of this research study will guide researchers in the field of Educational Data Mining (EDM) and practitioners on the most important dimensions to consider when evaluating social media applications for ubiquitous learning. © 2021 Caitlin Sam et al.