• Machine-learning techniques for the prediction of protein–protein interactions

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    • Keywords

       

      Clustering; deep learning; decision tree; machine-learning techniques; protein–protein interaction; support vector machine

    • Abstract

       

      Protein–protein interactions (PPIs) are important for the study of protein functions and pathways involved in differentbiological processes, as well as for understanding the cause and progression of diseases. Several high-throughput experimentaltechniques have been employed for the identification of PPIs in a few model organisms, but still, there is a huge gapin identifying all possible binary PPIs in an organism. Therefore, PPI prediction using machine-learning algorithms hasbeen used in conjunction with experimental methods for discovery of novel protein interactions. The two most popularsupervised machine-learning techniques used in the prediction of PPIs are support vector machines and random forestclassifiers. Bayesian-probabilistic inference has also been used but mainly for the scoring of high-throughput PPI datasetconfidence measures. Recently, deep-learning algorithms have been used for sequence-based prediction of PPIs. Severalclustering methods such as hierarchical and k-means are useful as unsupervised machine-learning algorithms for theprediction of interacting protein pairs without explicit data labelling. In summary, machine-learning techniques have beenwidely used for the prediction of PPIs thus allowing experimental researchers to study cellular PPI networks.

    • Author Affiliations

       

      DEBASREE SARKAR1 2 SUDIPTO SAHA2

      1. Present Address: SUNY Upstate Medical University, Syracuse, NY, USA
      2. Division of Bioinformatics, Bose Institute, Kolkata, India
    • Dates

       
  • Journal of Biosciences | News

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