Library Portal | UWC Portal | National ETDs | Global ETDs
    • Login
    Contact Us | About Us | FAQs | Login
    View Item 
    •   DSpace Home
    • Faculty of Natural Sciences
    • South African National Bioinformatics Institute (SANBI)
    • Research Articles (SANBI)
    • View Item
    •   DSpace Home
    • Faculty of Natural Sciences
    • South African National Bioinformatics Institute (SANBI)
    • Research Articles (SANBI)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Prediction of human-Bacillus anthracis protein–protein interactions using multi-layer neural network

    Thumbnail
    View/Open
    bty504 (1).pdf (387.0Kb)
    Date
    2018
    Author
    Ahmed, Ibrahim
    Witbooi, Peter
    Christoffels, Alan
    Metadata
    Show full item record
    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.
    URI
    10.1093/bioinformatics/bty504
    http://hdl.handle.net/10566/7072
    Collections
    • Research Articles (SANBI)

    DSpace 6.3 | Ubuntu | Copyright © University of the Western Cape
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     

    Browse

    All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    Login

    Statistics

    View Usage Statistics

    DSpace 6.3 | Ubuntu | Copyright © University of the Western Cape
    Contact Us | Send Feedback
    Theme by 
    Atmire NV