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Dual-Layer Strengthened Collaborative Topic Regression Modeling for Predicting Drug Sensitivity.

Hang Wang, Jianing Xi, Minghui Wang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |August 15, 2018
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    This study introduces a novel dual-layer strengthened collaborative topic regression (DS-CTR) model for predicting cancer drug sensitivity. The DS-CTR model significantly improves prediction accuracy, aiding in the faster discovery of new anti-cancer therapeutics.

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    Area of Science:

    • Oncology
    • Pharmacogenomics
    • Computational Biology

    Background:

    • Precision medicine in oncology relies on understanding drug sensitivities.
    • Cancer cell line screening provides valuable genomic and pharmacological data for drug response prediction.
    • Existing methods often focus solely on genomic features for drug response classification.

    Purpose of the Study:

    • To develop an innovative model for accurate drug sensitivity prediction in cancer.
    • To enhance prediction by integrating pharmacogenomics data and similarity networks.
    • To improve the efficiency and cost-effectiveness of discovering new anti-cancer therapeutics.

    Main Methods:

    • Developed a dual-layer strengthened collaborative topic regression (DS-CTR) model.
    • Utilized a graphic model to learn drug and cell line features from pharmacogenomics data.
    • Incorporated drug and cell line similarity networks to strengthen prediction correlations.

    Main Results:

    • The DS-CTR model demonstrated significantly improved drug response prediction performance.
    • Performance was validated using the Genomics of Drug Sensitivity in Cancer (GDSC) dataset.
    • Achieved superior results compared to four categories of state-of-the-art algorithms based on ROC and AUC metrics.

    Conclusions:

    • The DS-CTR model offers a novel approach to accurate drug sensitivity prediction.
    • The model effectively uncovers cell-drug associations supported by literature evidence.
    • DS-CTR has the potential to accelerate the discovery of anti-cancer therapeutics in preclinical trials.