De novo inference of clinically relevant gene function relationships from tumor RNA-seq remains a challenging task. Current methods typically either partition patient samples into a few subtypes or rely upon analysis of pairwise gene correlations that will miss some groups in noisy data. Leveraging higher dimensional information can be expected to increase the power to discern targetable pathways, but this is commonly thought to be an intractable computational problem.
Description
Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer | BMC Medical Genomics | Full Text
%0 Journal Article
%1 pepke2017comprehensive
%A Pepke, Shirley
%A Ver Steeg, Greg
%D 2017
%J BMC Medical Genomics
%K CorEx MUSTREAD READ fulltext gregversteeg software
%N 1
%P 12
%T Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer
%U http://dx.doi.org/10.1186/s12920-017-0245-6
%V 10
%X De novo inference of clinically relevant gene function relationships from tumor RNA-seq remains a challenging task. Current methods typically either partition patient samples into a few subtypes or rely upon analysis of pairwise gene correlations that will miss some groups in noisy data. Leveraging higher dimensional information can be expected to increase the power to discern targetable pathways, but this is commonly thought to be an intractable computational problem.
@article{pepke2017comprehensive,
abstract = {De novo inference of clinically relevant gene function relationships from tumor RNA-seq remains a challenging task. Current methods typically either partition patient samples into a few subtypes or rely upon analysis of pairwise gene correlations that will miss some groups in noisy data. Leveraging higher dimensional information can be expected to increase the power to discern targetable pathways, but this is commonly thought to be an intractable computational problem.},
added-at = {2017-04-17T21:47:15.000+0200},
author = {Pepke, Shirley and Ver Steeg, Greg},
biburl = {https://www.bibsonomy.org/bibtex/2fba89a53645e787079993ab950b8f228/marcsaric},
description = {Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer | BMC Medical Genomics | Full Text},
interhash = {c67f2b54d07c1907a92550c0dda2c475},
intrahash = {fba89a53645e787079993ab950b8f228},
journal = {BMC Medical Genomics},
keywords = {CorEx MUSTREAD READ fulltext gregversteeg software},
number = 1,
pages = 12,
timestamp = {2017-05-21T01:01:03.000+0200},
title = {Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer},
url = {http://dx.doi.org/10.1186/s12920-017-0245-6},
volume = 10,
year = 2017
}