文献类型： 会议论文

第一作者： IN SOCK JANG

作者： IN SOCK JANG ¹ ; ELIAS CHAIBUB NETO ¹ ; JUSTIN GUINNEY ¹ ; STEPHEN H. FRIEND ¹ ; ADAM A. MARGOLIN ¹ ;

作者机构： 1.Sage Bionetworks 1100 Fairview Ave. N Seattle, WA 98109, USA

关键词： Cancer cell lines;pharmacogenomics;machine learning;predictive modeling

会议名称： Pacific Symposium on Biocomputing

主办单位：

页码： 63-74

摘要： Large-scale pharmacogenomic screens of cancer cell lines have emerged as an attractive pre-clinical system for identifying tumor genetic subtypes with selective sensitivity to targeted therapeutic strategies. Application of modern machine learning approaches to pharmacogenomic datascts have demonstrated the ability to infer genomic predictors of compound sensitivity. Such modeling approaches entail many analytical design choices; however, a systematic study evaluating the relative performance attributable to each design choice is not yet available. In this work, we evaluated over 110,000 different models, based on a multifactorial experimental design testing systematic combinations of modeling factors within several categories of modeling choices, including: type of algorithm, type of molecular feature data, compound being predicted, method of summarizing compound sensitivity values, and whether predictions are based on discretized or continuous response values. Our results suggest that model input data (type of molecular features and choice of compound) are the primary factors explaining model performance, followed by choice of algorithm. Our results also provide a statistically principled set of recommended modeling guidelines, including: using elastic net or ridge regression with input features from all genomic profiling platforms, most importantly, gene expression features, to predict continuous-valued sensitivity scores summarized using the area under the dose response curve, with pathway targeted compounds most likely to yield the most accurate predictors. In addition, our study provides a publicly available resource of all modeling results, an open source code base, and experimental design for researchers throughout the community to build on our results and assess novel methodologies or applications in related predictive modeling problems.

分类号： q811