A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction

Abstract

Whereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database (https://www.gisaid.org/), between December 2019 and January 15, 2021, a total of 8864 human SARS-CoV-2 complete genome sequences processed by gender, across 6 continents (88 countries) of the world, Antarctica exempt, were analyzed. We hypothesized that data speak for itself and can discern true and explainable patterns of the disease. Identical genome diversity and pattern correlates analysis performed using a hybrid of biotechnology and machine learning methods corroborate the emergence of inter- and intra- SARS-CoV-2 sub-strains transmission and sustain an increase in sub-strains within the various continents, with nucleotide mutations dynamically varying between individuals in close association with the virus as it adapts to its host/environment. Interestingly, some viral sub-strain patterns progressively transformed into new sub-strain clusters indicating varying amino acid, and strong nucleotide association derived from same lineage. A novel cognitive approach to knowledge mining helped the discovery of transmission routes and seamless contact tracing protocol. Our classification results were better than state-of-the-art methods, indicating a more robust system for predicting emerging or new viral sub-strain(s). The results therefore offer explanations for the growing concerns about the virus and its next wave(s). A future direction of this work is a defuzzification of confusable pattern clusters for precise intra-country SARS-CoV-2 sub-strains analytics.

Kinerja Komparatif Optimasi Algoritma Naive Bayes dalam Klasifikasi Teks untuk Uji Klinis Kanker

“…Tiga algoritma Naive Bayes yaitu Bernoulli, Gaussian, dan Multinomial masing-masing memiliki kekuatan yang berbeda dan cocok untuk jenis data yang berbeda [18], yang membuat mereka menarik untuk dibandingkan dalam tugas klasifikasi teks seperti klasifikasi teks kanker. Bernoulli Naive Bayes yang bagus dalam menangani atribut boolean atau biner dimana algoritma ini bekerja dengan cara memodelkan ◼ 115 Kinerja Komparatif Optimasi Algoritma Naive Bayes dalam Klasifikasi Teks untuk Uji Klinis Kanker (Taslim) keberadaan atau ketiadaan fitur [19].…”

Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through Machine and Deep Learning techniques

“…Ekpenyong et al . —on the other hand proposed a computational approach for the identification of the continental origin of SARS-CoV-2 sub-strains and gender-specific isolates [ 21 ].…”