Improving Drug Sensitivity Prediction Using Different Types of Data

作者：HA Hejase, C. Chan · 发表于：CPT Pharmacometrics & Systems Pharmacology · 年份：2015 · DOI：10.1002/psp4.2 · 被引用次数：21 · 研究领域：Computational Drug Discovery Methods、Gene expression and cancer classification、Advanced Biosensing Techniques and Applications

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.