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Author: search.filters.author.Malik, Aijaz AhmadHas files: No

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    Item type:Publication,
    StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors
    (2021-10-01)
    Malik, Aijaz Ahmad
    ;
    Chotpatiwetchkul, Warot
    ;
    Phanus-umporn, Chuleeporn
    ;
    Nantasenamat, Chanin
    ;
    Charoenkwan, Phasit
    Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental methods is the gold standard method for the design and development of new HCV inhibitors, they often require costly investment of time and resources. In this study, we develop a novel machine learning-based meta-predictor (termed StackHCV) for accurate and large-scale identification of HCV inhibitors. Unlike the existing method, which is based on single-feature-based approach, we first constructed a pool of various baseline models by employing a wide range of heterogeneous molecular fingerprints with five popular machine learning algorithms (k-nearest neighbor, multi-layer perceptron, partial least squares, random forest and support vectors machine). Secondly, we integrated these baseline models in order to develop the final meta-based model by means of the stacking strategy. Extensive benchmarking experiments showed that StackHCV achieved a more accurate and stable performance as compared to its constituent baseline models on the training dataset and also outperformed the existing predictor on the independent test dataset. To facilitate the high-throughput identification of HCV inhibitors, we built a web server that can be freely accessed at http://camt.pythonanywhere.com/StackHCV. It is expected that StackHCV could be a useful tool for fast and precise identification of potential drugs against HCV NS5B particularly for liver cancer therapy and other clinical applications.
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    Item type:Publication,
    PepBio: Predicting the bioactivity of host defense peptides
    (2017-01-01)
    Simeon, Saw
    ;
    Li, Hao
    ;
    Win, Thet Su
    ;
    Malik, Aijaz Ahmad
    ;
    Kandhro, Abdul Hafeez
    Host defense peptides (HDPs) represents a class of ubiquitous and rapid responding immune molecules capable of direct inactivation of a wide range of pathogens. Recent research has shown HDPs to be promising candidates for development as a novel class of broad-spectrum chemotherapeutic agent that is effective against both pathogenic microbes and malignant neoplasm. This study aims to quantitatively explore the relationship between easy-to-interpret amino acid composition descriptors of HDPs with their respective bioactivities. Classification models were constructed using the C4.5 decision tree and random forest classifiers. Good predictive performance was achieved as deduced from the accuracy, sensitivity and specificity in excess of 90% and Matthews correlation coefficient in excess of 0.5 for all three evaluated data subsets (e.g. training, 10-fold cross-validation and external validation sets). The source code and data set used for the construction of classification models are available on GitHub at https://github.com/chaninn/pepbio/.