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Chris Gaiteri

Showing results (1-10 of 67) with videos related to

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Frontiers in Neuroscience|September 17, 2011
Differentially expressed genes in major depression reside on the periphery of resilient gene coexpression networksChris Gaiteri, Etienne Sibille
Frontiers in Computational Neuroscience|March 5, 2011
The interaction of intrinsic dynamics and network topology in determining network burst synchronyChris Gaiteri, Jonathan E Rubin
Nature Machine Intelligence|July 17, 2020
Deep learning decodes the principles of differential gene expressionShinya Tasaki, Chris Gaiteri, Sara Mostafavi, et al.
Plos One|April 9, 2010
Altered gene synchrony suggests a combined hormone-mediated dysregulated state in major depressionChris Gaiteri, Jean-Philippe Guilloux, David A Lewis, et al.
Genetics|January 30, 2015
Bayesian network reconstruction using systems genetics data: comparison of MCMC methodsShinya Tasaki, Ben Sauerwine, Bruce Hoff, et al.
Frontiers in Neuroscience|October 24, 2018
The Molecular and Neuropathological Consequences of Genetic Risk for Alzheimer's DementiaShinya Tasaki, Chris Gaiteri, Sara Mostafavi, et al.
Applied Network Science|December 12, 2018
Supporting novel biomedical research via multilayer collaboration networksKonstantin Kuzmin, Xiaoyan Lu, Partha Sarathi Mukherjee, et al.
Entropy (Basel, Switzerland)|August 26, 2023
Network Analytics Enabled by Generating a Pool of Network Variants from Noisy DataAamir Mandviwalla, Amr Elsisy, Muhammad Saad Atique, et al.
Nature Reviews. Neurology|June 11, 2016
Genetic variants in Alzheimer disease - molecular and brain network approachesChris Gaiteri, Sara Mostafavi, Christopher J Honey, et al.
Frontiers in Genetics|August 25, 2018
Multi-omic Directed Networks Describe Features of Gene Regulation in Aged Brains and Expand the Set of Genes Driving Cognitive DeclineShinya Tasaki, Chris Gaiteri, Sara Mostafavi, et al.
Pageof 7

Showing results (1-10 of 67) with videos related to

Sort By:
Pageof 7
Frontiers in Neuroscience|September 17, 2011
Differentially expressed genes in major depression reside on the periphery of resilient gene coexpression networksChris Gaiteri, Etienne Sibille
Frontiers in Computational Neuroscience|March 5, 2011
The interaction of intrinsic dynamics and network topology in determining network burst synchronyChris Gaiteri, Jonathan E Rubin
Nature Machine Intelligence|July 17, 2020
Deep learning decodes the principles of differential gene expressionShinya Tasaki, Chris Gaiteri, Sara Mostafavi, et al.
Plos One|April 9, 2010
Altered gene synchrony suggests a combined hormone-mediated dysregulated state in major depressionChris Gaiteri, Jean-Philippe Guilloux, David A Lewis, et al.
Genetics|January 30, 2015
Bayesian network reconstruction using systems genetics data: comparison of MCMC methodsShinya Tasaki, Ben Sauerwine, Bruce Hoff, et al.
Frontiers in Neuroscience|October 24, 2018
The Molecular and Neuropathological Consequences of Genetic Risk for Alzheimer's DementiaShinya Tasaki, Chris Gaiteri, Sara Mostafavi, et al.
Applied Network Science|December 12, 2018
Supporting novel biomedical research via multilayer collaboration networksKonstantin Kuzmin, Xiaoyan Lu, Partha Sarathi Mukherjee, et al.
Entropy (Basel, Switzerland)|August 26, 2023
Network Analytics Enabled by Generating a Pool of Network Variants from Noisy DataAamir Mandviwalla, Amr Elsisy, Muhammad Saad Atique, et al.
Nature Reviews. Neurology|June 11, 2016
Genetic variants in Alzheimer disease - molecular and brain network approachesChris Gaiteri, Sara Mostafavi, Christopher J Honey, et al.
Frontiers in Genetics|August 25, 2018
Multi-omic Directed Networks Describe Features of Gene Regulation in Aged Brains and Expand the Set of Genes Driving Cognitive DeclineShinya Tasaki, Chris Gaiteri, Sara Mostafavi, et al.
Pageof 7