Quantifying Mutational Response to Track the Evolution of SARS-CoV-2 Spike Variants: Introducing a Statistical-Mechanics-Guided Machine Learning Method-Reference-Cited by-同舟云学术

Quantifying Mutational Response to Track the Evolution of SARS-CoV-2 Spike Variants: Introducing a Statistical-Mechanics-Guided Machine Learning Method

Published:2022-09-30 Issue:40 Volume:126 Page:7895-7905
ISSN:1520-6106
Container-title:The Journal of Physical Chemistry B
language:en
Short-container-title:J. Phys. Chem. B

Author:

Sangeet Satyam¹,Sarkar Raju¹,Mohanty Saswat K.¹^ORCID,Roy Susmita¹^ORCID

Affiliation:

1. Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Kolkata, West Bengal741246, India

Funder

Science and Engineering Research Board

Department of Biotechnology , Ministry of Science and Technology

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Surfaces, Coatings and Films,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcb.2c04574

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1. A Novel Coronavirus from Patients with Pneumonia in China, 2019

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5. An Introduction to the Physical Theories of Equilibrium Thermostatics and Irreversible Thermodynamics

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