Improving drug sensitivity prediction using different types of data

H. A. Hejase, C. Chan

Research output: Contribution to journalArticle

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)
Article number666
JournalCPT: Pharmacometrics and Systems Pharmacology
Volume3
Issue number5
DOIs
StatePublished - 2014

Profile

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

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. 3, No. 5, 666, 2014.

Research output: Contribution to journalArticle

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