About: Analyzing hCov genome sequences: Applying Machine Intelligence and beyond

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Analyzing hCov genome sequences: Applying Machine Intelligence and beyond
Creator	Rahman, Rahman, M Anjum, Naser Haisam, Muhammad Rafid, Mohammad Saha, Arpita Sawmya, Shashata Tasnim, Sadia Toufikuzzaman, Ali
Source	BioRxiv
abstract	Covid-19 pandemic, caused by the sars-cov-2 strain 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 analysed. We present here an analysis pipeline comprising phylogenetic analysis on strains of this novel virus to track its evolutionary history among the countries uncovering several interesting relationships, followed by a classification exercise to identify the virulence of the strains and extraction of important features from its genetic material that are used subsequently to predict mutation at those interesting sites using deep learning techniques. 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.
has issue date	2020-06-03(xsd:dateTime)
bibo:doi	10.1101/2020.06.03.131987
has license	biorxiv
sha1sum (hex)	ed6efa757ae5582f4a6108e97d2f40c7cb320584
schema:url	https://doi.org/10.1101/2020.06.03.131987
resource representing a document's title	Analyzing hCov genome sequences: Applying Machine Intelligence and beyond
schema:publication	bioRxiv
resource representing a document's body	covid:ed6efa757ae5582f4a6108e97d2f40c7cb320584#body_text
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