Prediction of human-Bacillus anthracis protein–protein interactions using multi-layer neural network
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Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
Oxford University Press
Abstract
Triplet amino acids have successfully been included in feature selection to predict
human-HPV protein-protein interactions (PPI). The utility of supervised learning methods is
curtailed due to experimental data not being available in sufficient quantities. Improvements in
machine learning techniques and features selection will enhance the study of PPI between host
and pathogen.We present a comparison of a neural network model versus SVM for prediction of hostpathogen PPI based on a combination of features including: amino acid quadruplets, pairwise sequence similarity, and human interactome properties. The neural network and SVM were implemented using Python Sklearn library. The neural network model using quadruplet features and
other network features outperformance the SVM model. The models are tested against published
predictors and then applied to the human-B.anthracis case. Gene ontology term enrichment analysis identifies immunology response and regulation as functions of interacting proteins. For prediction of Human-viral PPI, our model (neural network) is a significant improvement in overall performance compared to a predictor using the triplets feature and achieves a good accuracy in
predicting human-B.anthracis PPI.
Description
Keywords
Human-Bacillus anthracis, Neural network, Amino acids, Protein, Bioinformatics
Citation
Ahmed, I. et al. (2018). Prediction of human-Bacillus anthracis protein–protein interactions using multi-layer neural network. Bioinformatics, 34(24), 4159-4164. 10.1093/bioinformatics/bty504