Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?

Journal: Journal of molecular biology
Published Date: Jun 11, 2022

Abstract

MOTIVATION: Continuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of variants and huge scale of genomic data have added to the challenges of tracing the mutations/variants and their relationship to infection severity (if any).

Authors

  • Sunil Nagpal
    TCS Research, Tata Consultancy Services Ltd, Pune 411 013, India; CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi 110 025, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201 002, India. Electronic address: https://twitter.com/NagpalSun.
  • Nishal Kumar Pinna
    Tata Consultancy Services Ltd, Pune 411013, India. Electronic address: https://twitter.com/nishal_pinna.
  • Namrata Pant
    Tata Consultancy Services Ltd, Pune 411013, India.
  • Rohan Singh
    TCS Research, Tata Consultancy Services Ltd, Pune 411 013, India.
  • Divyanshu Srivastava
    Tata Consultancy Services Ltd, Pune 411013, India. Electronic address: https://twitter.com/divy2926.
  • Sharmila S Mande
    TCS Research, Tata Consultancy Services Ltd, Pune 411 013, India. Electronic address: sharmila.mande@tcs.com.

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