web analytics

Machine Learning-Based Prediction Of Viral Mutations From Genomic Sequences

Published:
Lead Inventor: Ishanu Chattopadhyay
Print or Save to PDF

SUMMARY

  • There have been significant research efforts to develop machine learning and predictive analytics based tools to predict viral evolution which remains the main obstacle in the early detection of drug-resistant strains and facilitate the design of more efficient antiviral treatments.
  • Machine learning and advanced algorithms have facilitated the study of structure-activity relationships, secondary and tertiary structure evolution prediction, and sequence error correction.
  • This technology includes a software tool for predicting the dominant circulating strain of evolving pathogens like influenza and help with vaccine design.
  • The underlying algorithm in this invention reverse‐engineers the laws driving evolutionary changes in evolving pathogens to predict future mutations and emerging strains.

FIGURE

ADVANTAGES

ADVANTAGES

  • First of its kind prediction approach for evolution of viruses

  • Performs quantitative assessment indicative of the risk of viral emergence based on strains that circulate in the wild.   

  • This tool predicts dominant strains of future seasonal epidemics significantly better than the WHO recommendations used today in flu shot compositions.

APPLICATIONS

  • Prediction of dominant strains of future seasonal epidemics significantly better than the WHO recommendations used today in flu shot compositions.

  • Vaccine development focused on new emerging strains of viruses.

  • The software can also be used by health agencies like CDC to take preventive measures and issue guidelines based on new strains and potential impact on population. 

PUBLICATIONS