Improving drug sensitivity prediction using different types of data

H. A. Hejase, C. Chan

    Research output: Research - peer-reviewArticle

    • 2 Citations

    Abstract

    The algorithms and models used to address the two subchallenges that are part of the NCI-DREAM (Dialogue for Reverse Engineering Assessments and Methods) Drug Sensitivity Prediction Challenge (2012) are presented. In subchallenge 1, a bidirectional search algorithm is introduced and optimized using an ensemble scheme and a nonlinear support vector machine (SVM) is then applied to predict the effects of the drug compounds on breast cancer cell lines. In subchallenge 2, a weighted Euclidean distance method is introduced to predict and rank the drug combinations from the most to the least effective in reducing the viability of a diffuse large B-cell lymphoma (DLBCL) cell line.

    LanguageEnglish (US)
    Pages98-105
    Number of pages8
    JournalCPT: Pharmacometrics and Systems Pharmacology
    Volume4
    Issue number2
    DOIs
    StatePublished - Feb 1 2015

    Profile

    Drugs
    Prediction
    Cell Line
    Pharmaceutical Preparations
    Cells
    Predict
    Line
    Cell
    Lymphoma, Large B-Cell, Diffuse
    Drug Combinations
    Breast Neoplasms
    Support Vector Machine
    B Cells
    Reverse Engineering
    Euclidean Distance
    Breast Cancer
    Viability
    Search Algorithm
    Ensemble
    Model

    ASJC Scopus subject areas

    • Cardiology and Cardiovascular Medicine

    Cite this

    Improving drug sensitivity prediction using different types of data. / Hejase, H. A.; Chan, C.

    In: CPT: Pharmacometrics and Systems Pharmacology, Vol. 4, No. 2, 01.02.2015, p. 98-105.

    Research output: Research - peer-reviewArticle

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