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BMC Systems Biology
|
January 28, 2009
Bifurcation analysis informs Bayesian inference in the Hes1 feedback loop
Catherine F Higham
Scientific Reports
|
March 9, 2023
Quantum deep learning by sampling neural nets with a quantum annealer
Catherine F Higham, Adrian Bedford
BMC Bioinformatics
|
November 26, 2013
A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana
Catherine F Higham, Dirk Husmeier
Journal of the Royal Society, Interface
|
September 20, 2013
Modelling and inference reveal nonlinear length-dependent suppression of somatic instability for small disease associated alleles in myotonic dystrophy type 1 and Huntington disease
Catherine F Higham, Darren G Monckton
Frontiers in Artificial Intelligence
|
March 18, 2021
Deep Learnability: Using Neural Networks to Quantify Language Similarity and Learnability
Clara Cohen, Catherine F Higham, Syed Waqar Nabi
Scientific Reports
|
February 7, 2018
Deep learning for real-time single-pixel video
Catherine F Higham, Roderick Murray-Smith, Miles J Padgett, et al.
Human Molecular Genetics
|
February 28, 2012
High levels of somatic DNA diversity at the myotonic dystrophy type 1 locus are driven by ultra-frequent expansion and contraction mutations
Catherine F Higham, Fernando Morales, Christina A Cobbold, et al.
Scientific Reports
|
August 11, 2018
Neural network identification of people hidden from view with a single-pixel, single-photon detector
Piergiorgio Caramazza, Alessandro Boccolini, Daniel Buschek, et al.
Human Molecular Genetics
|
May 19, 2012
Somatic instability of the expanded CTG triplet repeat in myotonic dystrophy type 1 is a heritable quantitative trait and modifier of disease severity
Fernando Morales, Jillian M Couto, Catherine F Higham, et al.
Page
of 1
Search research articles
Search
Showing results (1-10 of 9) with videos related to
Sort By:
Page
of 1
BMC Systems Biology
|
January 28, 2009
Bifurcation analysis informs Bayesian inference in the Hes1 feedback loop
Catherine F Higham
Scientific Reports
|
March 9, 2023
Quantum deep learning by sampling neural nets with a quantum annealer
Catherine F Higham, Adrian Bedford
BMC Bioinformatics
|
November 26, 2013
A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana
Catherine F Higham, Dirk Husmeier
Journal of the Royal Society, Interface
|
September 20, 2013
Modelling and inference reveal nonlinear length-dependent suppression of somatic instability for small disease associated alleles in myotonic dystrophy type 1 and Huntington disease
Catherine F Higham, Darren G Monckton
Frontiers in Artificial Intelligence
|
March 18, 2021
Deep Learnability: Using Neural Networks to Quantify Language Similarity and Learnability
Clara Cohen, Catherine F Higham, Syed Waqar Nabi
Scientific Reports
|
February 7, 2018
Deep learning for real-time single-pixel video
Catherine F Higham, Roderick Murray-Smith, Miles J Padgett, et al.
Human Molecular Genetics
|
February 28, 2012
High levels of somatic DNA diversity at the myotonic dystrophy type 1 locus are driven by ultra-frequent expansion and contraction mutations
Catherine F Higham, Fernando Morales, Christina A Cobbold, et al.
Scientific Reports
|
August 11, 2018
Neural network identification of people hidden from view with a single-pixel, single-photon detector
Piergiorgio Caramazza, Alessandro Boccolini, Daniel Buschek, et al.
Human Molecular Genetics
|
May 19, 2012
Somatic instability of the expanded CTG triplet repeat in myotonic dystrophy type 1 is a heritable quantitative trait and modifier of disease severity
Fernando Morales, Jillian M Couto, Catherine F Higham, et al.
Page
of 1