scholarly journals Deciphering H3K4me3 broad domains associated with gene-regulatory networks and conserved epigenomic landscapes in the human brain

2015 ◽  
Vol 5 (11) ◽  
pp. e679-e679 ◽  
Author(s):  
A Dincer ◽  
D P Gavin ◽  
K Xu ◽  
B Zhang ◽  
J T Dudley ◽  
...  
Keyword(s):  
Human Brain ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Gene Regulatory

scholarly journals Deciphering Neuronal Broad Histone H3k4me3 Domains Associated with Gene-Regulatory Networks and Conserved Epigenomic Landscapes In The Human Brain

2017 ◽  
Vol 145 ◽  
pp. S142
Author(s):  
Aslihan Dincer ◽  
Bin Zhang ◽  
Joel Dudley ◽  
David Gavin ◽  
Eric Schadt ◽  
...  
Keyword(s):  
Human Brain ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Gene Regulatory

scholarly journals Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian Networks

2020 ◽  
Author(s):  
Nikolas Bernaola ◽  
Mario Michiels ◽  
Pedro Larrañaga ◽  
Concha Bielza
Keyword(s):  
Human Brain ◽  
Bayesian Networks ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Search Algorithm ◽  
Experimental Tests ◽  
Brain Structures ◽  
Brain Atlas ◽  
Gene Regulatory ◽  
Using Data

AbstractWe present FGES-Merge, a new method for learning the structure of gene regulatory networks via merging locally learned Bayesian networks, based on the fast greedy equivalent search algorithm. The method is competitive with the state of the art in terms of the recall of the true structure while also improving upon it in terms of speed, scaling up to the tens of thousands of variables and being able to use empirical knowledge about the topological structure of gene regulatory networks. We apply this method to learning the gene regulatory network for the full human genome using data from samples of different brain structures (from the Allen Human Brain Atlas). Our goal is to develop a Bayesian network model that predicts interactions between genes in a way that is clear to experts, following the current trends in interpretable artificial intelligence. To achieve this, we also present a new open-access visualization tool that facilitates the exploration of massive networks and can aid in finding nodes of interest for experimental tests.

scholarly journals Activity-Dependent Human Brain Coding/Noncoding Gene Regulatory Networks

Genetics ◽  
2012 ◽  
Vol 192 (3) ◽  
pp. 1133-1148 ◽  
Author(s):  
Leonard Lipovich ◽  
Fabien Dachet ◽  
Juan Cai ◽  
Shruti Bagla ◽  
Karina Balan ◽  
...  
Keyword(s):  
Human Brain ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Gene Regulatory ◽  
Activity Dependent
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