Extending Cancer Cell Drug Response Models
نویسندگان
چکیده
This project examines methods for predicting the degree to which various drugs inhibit the growth of a range of cancer cell types. Data consist of over 40,000 features for each of 432 cell lines and cell growth inhibition measurements for 24 drugs. The high dimensionality of this data raises challenges that are addressed through Elastic Net parameter tuning. We also investigate Principal Component Analysis and the use of principal components as features to reduce dimensionality without compromising accuracy. Finally, we explore Support Vector Regression and Random Tree models for this data.
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