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Pathway-Based Genomics Prediction using Generalized Elastic Net
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DOI：10.1371/journal.pcbi.1004790.png)
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摘要
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We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/ HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach.
Keyword:
REGULARIZATION PATHS
COORDINATE DESCENT
VARIABLE SELECTION
GENE-EXPRESSION
CANCER-PATIENTS
CLASSIFICATION
KNOWLEDGE
MODEL
DISCRIMINANT
ALGORITHMS
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7.2
论文数:
2.1K
被引数:
3.9W
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Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data miningIF0
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