title: Improving Drug Sensitivity Prediction Using Different Types of Data.
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.
authors.affiliation: Department of Computer Science and Engineering, Michigan State University East Lansing, Michigan, USA.
authors.forename: H A
authors.initials: HA
authors.lastname: Hejase
authors.affiliation: Department of Computer Science and Engineering, Michigan State University East Lansing, Michigan, USA ; Department of Chemical Engineering and Materials Science, Michigan State University East Lansing, Michigan, USA ; Department of Biochemistry and Molecular Biology, Michigan State University East Lansing, Michigan, USA.
authors.forename: C
authors.initials: C
authors.lastname: Chan