Prediction of Protein-Protein Interaction Using Machine Learning

Abstract

The tremendous of bioactivities are directed by protein association, which is an organization of protein interconnections. A healthy biota relies significantly on regulated connections between protein complexes, and any abnormal relations can result in diseases such cervical leukaemia, TB, and other neurological ailments. Over the years, a slew of computer approaches for analysing and predicting Protein-Protein Interaction have been developed; nevertheless, the bulk of these strategies have proven to be time-consuming and costly. As a result, the requirement for faster, more efficient, more important Protein-Protein Interaction analysis justifies the development of Machine Learning (ML) techniques like NB. These classifiers are useful in the unfolding of Interacting Proteins items in order of amino acid sequence data. The NB classifier, in particular, is capable of addressing a wide range of complex classification issues while still delivering reliable answers in a reasonable amount of time. This paper highlights various state-of-the-art NB-based Protein-Protein Interaction experiments as well as the obstacles that have been encountered when using the NB approach.