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Plos Computational Biology
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October 15, 2016
Mechanistic Modelling and Bayesian Inference Elucidates the Variable Dynamics of Double-Strand Break Repair
Mae L Woods, Chris P Barnes
Biochimica Et Biophysica Acta. Reviews on Cancer
|
January 11, 2017
Catch my drift? Making sense of genomic intra-tumour heterogeneity
Andrea Sottoriva, Chris P Barnes, Trevor A Graham
Interface Focus
|
December 11, 2012
Bayesian design strategies for synthetic biology
Chris P Barnes, Daniel Silk, Michael P H Stumpf
Nature Communications
|
March 29, 2025
Cell-cycle dependent DNA repair and replication unifies patterns of chromosome instability
Bingxin Lu, Samuel Winnall, William Cross, et al.
Nature Communications
|
January 29, 2021
Automated design of synthetic microbial communities
Behzad D Karkaria, Alex J H Fedorec, Chris P Barnes
Plos Computational Biology
|
November 21, 2022
Deep reinforcement learning for optimal experimental design in biology
Neythen J Treloar, Nathan Braniff, Brian Ingalls, et al.
ACS Synthetic Biology
|
February 3, 2016
A Statistical Approach Reveals Designs for the Most Robust Stochastic Gene Oscillators
Mae L Woods, Miriam Leon, Ruben Perez-Carrasco, et al.
Proceedings of the National Academy of Sciences of the United States of America
|
August 31, 2011
Bayesian design of synthetic biological systems
Chris P Barnes, Daniel Silk, Xia Sheng, et al.
Cell Systems
|
July 27, 2018
Synthetic Biology and Engineered Live Biotherapeutics: Toward Increasing System Complexity
Tanel Ozdemir, Alex J H Fedorec, Tal Danino, et al.
Statistical Applications in Genetics and Molecular Biology
|
March 19, 2013
On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo
Sarah Filippi, Chris P Barnes, Julien Cornebise, et al.
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of 7
Search research articles
Search
Showing results (1-10 of 64) with videos related to
Sort By:
Page
of 7
Plos Computational Biology
|
October 15, 2016
Mechanistic Modelling and Bayesian Inference Elucidates the Variable Dynamics of Double-Strand Break Repair
Mae L Woods, Chris P Barnes
Biochimica Et Biophysica Acta. Reviews on Cancer
|
January 11, 2017
Catch my drift? Making sense of genomic intra-tumour heterogeneity
Andrea Sottoriva, Chris P Barnes, Trevor A Graham
Interface Focus
|
December 11, 2012
Bayesian design strategies for synthetic biology
Chris P Barnes, Daniel Silk, Michael P H Stumpf
Nature Communications
|
March 29, 2025
Cell-cycle dependent DNA repair and replication unifies patterns of chromosome instability
Bingxin Lu, Samuel Winnall, William Cross, et al.
Nature Communications
|
January 29, 2021
Automated design of synthetic microbial communities
Behzad D Karkaria, Alex J H Fedorec, Chris P Barnes
Plos Computational Biology
|
November 21, 2022
Deep reinforcement learning for optimal experimental design in biology
Neythen J Treloar, Nathan Braniff, Brian Ingalls, et al.
ACS Synthetic Biology
|
February 3, 2016
A Statistical Approach Reveals Designs for the Most Robust Stochastic Gene Oscillators
Mae L Woods, Miriam Leon, Ruben Perez-Carrasco, et al.
Proceedings of the National Academy of Sciences of the United States of America
|
August 31, 2011
Bayesian design of synthetic biological systems
Chris P Barnes, Daniel Silk, Xia Sheng, et al.
Cell Systems
|
July 27, 2018
Synthetic Biology and Engineered Live Biotherapeutics: Toward Increasing System Complexity
Tanel Ozdemir, Alex J H Fedorec, Tal Danino, et al.
Statistical Applications in Genetics and Molecular Biology
|
March 19, 2013
On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo
Sarah Filippi, Chris P Barnes, Julien Cornebise, et al.
Page
of 7