Machine learning techniques for sequence-based prediction of viral–host interactions between SARS-CoV-2 and human proteins-Reference-Cited by-同舟云学术

Machine learning techniques for sequence-based prediction of viral–host interactions between SARS-CoV-2 and human proteins

Published:2020-10 Issue:5 Volume:43 Page:438-450
ISSN:2319-4170
Container-title:Biomedical Journal
language:en
Short-container-title:Biomedical Journal

Author:

Dey Lopamudra,Chakraborty Sanjay,Mukhopadhyay Anirban^ORCID

Funder

Department of Science & Technology and Biotechnology, Government of West Bengal

Publisher

Elsevier BV

Subject

General Medicine

Reference51 articles.

1. COVIDiagnosis-Net: deep Bayes-SqueezeNet based diagnostic of the coronavirus disease 2019 (COVID-19) from X-Ray images;Ucar;Med Hypotheses,2020

2. COVID-19 spike-host cell receptor GRP78 binding site prediction;Ibrahim;J Infect,2020

3. Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: a machine learning-based approach;Kassani,2020

4. Accurate prediction of COVID-19 using chest X-Ray images through deep feature learning model with SMOTE and machine learning classifiers;Kumar,2020

5. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing;Gordon;Nature,2020

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