Molecular causes of transcriptional response: a Bayesian prior knowledge approach
Author(s) -
Kourosh Zarringhalam,
Ahmed Enayetallah,
Alex Gutteridge,
Ben S. Sidders,
Daniel Ziemek
Publication year - 2013
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btt557
Subject(s) - computational biology , context (archaeology) , bayesian network , causal inference , bayesian probability , biological network , inference , set (abstract data type) , computer science , biology , machine learning , artificial intelligence , mathematics , econometrics , paleontology , programming language
The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes.
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