%0 Journal Article %1 sokolov2016pathwaybased %A Sokolov, Artem %A Carlin, Daniel E. %A Paull, Evan O. %A Baertsch, Robert %A Stuart, Joshua M. %D 2016 %I Public Library of Science %J PLOS Computational Biology %K background elastic en generalized ien information net penalization %N 3 %P 1-23 %R 10.1371/journal.pcbi.1004790 %T Pathway-Based Genomics Prediction using Generalized Elastic Net %U https://doi.org/10.1371/journal.pcbi.1004790 %V 12 %X 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.

@article{sokolov2016pathwaybased, abstract = {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.}, added-at = {2019-06-26T04:06:10.000+0200}, author = {Sokolov, Artem and Carlin, Daniel E. and Paull, Evan O. and Baertsch, Robert and Stuart, Joshua M.}, biburl = {https://www.bibsonomy.org/bibtex/2d2ad7d57dd447f9029be308a635759cd/becker}, description = {Pathway-Based Genomics Prediction using Generalized Elastic Net}, doi = {10.1371/journal.pcbi.1004790}, interhash = {00404c20fd0fabab3e419cab58f8fbfe}, intrahash = {d2ad7d57dd447f9029be308a635759cd}, journal = {PLOS Computational Biology}, keywords = {background elastic en generalized ien information net penalization}, month = {03}, number = 3, pages = {1-23}, publisher = {Public Library of Science}, timestamp = {2019-06-26T04:06:30.000+0200}, title = {Pathway-Based Genomics Prediction using Generalized Elastic Net}, url = {https://doi.org/10.1371/journal.pcbi.1004790}, volume = 12, year = 2016 }