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Pathway-Based Genomics Prediction using Generalized Elastic Net

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  • Artem Sokolov
  • Daniel E Carlin
  • Evan O Paull
  • Robert Baertsch
  • Joshua M Stuart

Abstract

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.Author Summary: The low costs of sequencing and other high-throughput technologies have made available large amounts of data to address molecular biology problems. However, often this means thousands of measurements, for example on gene expression, are assayed for a much smaller number of samples. The imbalance complicates the identification of genes that generalize to new samples and in finding a collection of genes that suggest a theme for interpreting the data. Pathway and network-based approaches have proven their worth in these situations. They force solutions onto known biology and they produce more robust predictors. In this manuscript, we describe a new formulation of statistical learning approaches that naturally incorporates gene-gene relationships, like those found in gene network databases. The theory we present helps unify and codify an explicit formulation for gene pathway-informed machine-learning that should have wide reach.

Suggested Citation

  • Artem Sokolov & Daniel E Carlin & Evan O Paull & Robert Baertsch & Joshua M Stuart, 2016. "Pathway-Based Genomics Prediction using Generalized Elastic Net," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-23, March.
  • Handle: RePEc:plo:pcbi00:1004790
    DOI: 10.1371/journal.pcbi.1004790
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    References listed on IDEAS

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    1. Rosa A. Schiavo & David J. Hand, 2000. "Ten More Years of Error Rate Research," International Statistical Review, International Statistical Institute, vol. 68(3), pages 295-310, December.
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    4. Matt Silver & Peng Chen & Ruoying Li & Ching-Yu Cheng & Tien-Yin Wong & E-Shyong Tai & Yik-Ying Teo & Giovanni Montana, 2013. "Pathways-Driven Sparse Regression Identifies Pathways and Genes Associated with High-Density Lipoprotein Cholesterol in Two Asian Cohorts," PLOS Genetics, Public Library of Science, vol. 9(11), pages 1-28, November.
    5. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    6. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    7. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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    1. Reid Priedhorsky & Ashlynn R Daughton & Martha Barnard & Fiona O’Connell & Dave Osthus, 2019. "Estimating influenza incidence using search query deceptiveness and generalized ridge regression," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-23, October.
    2. Edward W Huang & Ameya Bhope & Jing Lim & Saurabh Sinha & Amin Emad, 2020. "Tissue-guided LASSO for prediction of clinical drug response using preclinical samples," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-22, January.

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