Author: Sawmya, Shashata; Saha, Arpita; Tasnim, Sadia; Toufikuzzaman, Md.; Anjum, Naser; Rafid, Ali Haisam Muhammad; Rahman, M. Saifur; Rahman, M. Sohel
Title: Analyzing hCov Genome Sequences: Predicting Virulence and Mutation Cord-id: 0x90yubt Document date: 2021_4_20
ID: 0x90yubt
Snippet: Background Covid-19 pandemic, caused by the SARS-CoV-2 genome sequence of coronavirus, has affected millions of people all over the world and taken thousands of lives. It is of utmost importance that the character of this deadly virus be studied and its nature be analyzed. Methods We present here an analysis pipeline comprising a classification exercise to identify the virulence of the genome sequences and extraction of important features from its genetic material that are used subsequently to p
Document: Background Covid-19 pandemic, caused by the SARS-CoV-2 genome sequence of coronavirus, has affected millions of people all over the world and taken thousands of lives. It is of utmost importance that the character of this deadly virus be studied and its nature be analyzed. Methods We present here an analysis pipeline comprising a classification exercise to identify the virulence of the genome sequences and extraction of important features from its genetic material that are used subsequently to predict mutation at those interesting sites using deep learning techniques. Results We have classified the SARS-CoV-2 genome sequences with high accuracy and predicted the mutations in the sites of Interest. Conclusions In a nutshell, we have prepared an analysis pipeline for hCov genome sequences leveraging the power of machine intelligence and uncovered what remained apparently shrouded by raw data.
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