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.
Beschreibung
Pathway-Based Genomics Prediction using Generalized Elastic Net
%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
}