A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways.

Journal: Methods in molecular biology (Clifton, N.J.)
PMID:

Abstract

The integrative method approaches are continuously evolving to provide accurate insights from the data that is received through experimentation on various biological systems. Multi-omics data can be integrated with predictive machine learning algorithms in order to provide results with high accuracy. This protocol chapter defines the steps required for the ML-multi-omics integration methods that are applied on biological datasets for its analysis and the visual interpretation of the results thus obtained.

Authors

  • Vidya Niranjan
    Department of Biotechnology, R V College of Engineering, Mysuru Road, Kengeri, Bengaluru, India. vidya.n@rvce.edu.in.
  • Akshay Uttarkar
    Department of Biotechnology, R V College of Engineering, Mysuru Road, Kengeri, Bengaluru, India.
  • Aakaanksha Kaul
    Department of Biotechnology, R V College of Engineering, Mysuru Road, Kengeri, Bengaluru, India.
  • Maryanne Varghese
    Department of Biotechnology, R V College of Engineering, Mysuru Road, Kengeri, Bengaluru, India.