UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Volume 8 Issue 3
March-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2103119


Registration ID:
306750

Page Number

888-895

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Title

Machine Learning Techniques for Sequence-Based Prediction of Viral-Host Interactions

Abstract

In predicting the PPIs between the virus and human proteins, different machine learning models have been developed that are further verified using biological trials. Their classification techniques are consistent with the predictions of various sequence-based human protein properties, such as the structure of amino acids, the structure of pseudo amino acids, and correlational triads. This paper will focus on the SARS-CoV-2 virus, hepatitis E virus-human, and hepatitis B virus-human PPIs to illustrate how machine learning techniques are used to predict a sequence-based viral to host interaction. This research can promote the detection of possible targets for the more efficient production of anti-viral drugs, which have now affected the entire world. The basic well-known automated machine learning methods are widely implemented to predict PPIs, such as Random Forest, Naïve Bayes, and SVM, are used to determine the output measure focused on five-fold methods of cross-validations. In the last phase of viral infections in the host, protein-protein interactions (PPIs) play an important role. Human cells, however, are made up of a vast number of proteins. Consequently, it is just not possible to verify all different combinations of interactions through laboratory experiments. It contributes to the emergence of different computational techniques to predict and further verify PPIs in between virus and human proteins through biological experimentation. For drug usage, an understanding of how the PPIs virus proteins associate with cells of the host for reproduction and survival is important. One way that the virus associates with its host is Protein-Protein Inter-action (PPI). Therefore, predicting the PPIs between both the host and viral proteins helps in explaining how well the disease replicates and induces those virus proteins. This paper will illustrate how machine learning is used in predicting the sequence of a viral to host interaction by considering the case of SARS-CoV-2 virus, hepatitis E virus-human, and hepatitis B virus-human PPIs virus.

Key Words

Machine Learning, Supervised classification, SARS-CoV-2 virus, hepatitis E virus-human, hepatitis B virus-human, Protein-protein interaction.

Cite This Article

"Machine Learning Techniques for Sequence-Based Prediction of Viral-Host Interactions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 3, page no.888-895, March-2021, Available :http://www.jetir.org/papers/JETIR2103119.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Machine Learning Techniques for Sequence-Based Prediction of Viral-Host Interactions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 3, page no. pp888-895, March-2021, Available at : http://www.jetir.org/papers/JETIR2103119.pdf

Publication Details

Published Paper ID: JETIR2103119
Registration ID: 306750
Published In: Volume 8 | Issue 3 | Year March-2021
DOI (Digital Object Identifier):
Page No: 888-895
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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