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Frank R Burden

Showing results (1-10 of 19) with videos related to

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Methods in Molecular Biology (Clifton, N.J.)|October 3, 2002
Application of neural networks to large dataset QSAR, virtual screening, and library designDavid A Winkler, Frank R Burden
Journal of Chemical Information and Modeling|July 10, 2015
Relevance Vector Machines: Sparse Classification Methods for QSARFrank R Burden, David A Winkler
Journal of Molecular Graphics & Modelling|May 10, 2005
Predictive Bayesian neural network models of MHC class II peptide bindingFrank R Burden, David A Winkler
Journal of Molecular Graphics & Modelling|June 9, 2004
Modelling blood-brain barrier partitioning using Bayesian neural netsDavid A Winkler, Frank R Burden
Molecular Biosystems|January 28, 2012
Robust, quantitative tools for modelling ex-vivo expansion of haematopoietic stem cells and progenitorsDavid A Winkler, Frank R Burden
Methods in Molecular Biology (Clifton, N.J.)|May 3, 2008
Nonlinear predictive modeling of MHC class II-peptide binding using Bayesian neural networksDavid A Winkler, Frank R Burden
Journal of Medicinal Chemistry|November 30, 2004
Broad-based quantitative structure-activity relationship modeling of potency and selectivity of farnesyltransferase inhibitors using a Bayesian regularized neural networkMitchell J Polley, David A Winkler, Frank R Burden
Journal of Chemical Information and Modeling|May 13, 2009
Toward novel universal descriptors: charge fingerprintsFrank R Burden, Mitchell J Polley, David A Winkler
Stem Cell Research|April 28, 2009
Stem cell decision making and critical-like exploratory networksJulianne D Halley, Frank R Burden, David A Winkler
Stem Cell Research|April 23, 2009
Toward a Rosetta stone for the stem cell genome: stochastic gene expression, network architecture, and external influencesJulianne D Halley, David A Winkler, Frank R Burden
Pageof 2

Showing results (1-10 of 19) with videos related to

Sort By:
Pageof 2
Methods in Molecular Biology (Clifton, N.J.)|October 3, 2002
Application of neural networks to large dataset QSAR, virtual screening, and library designDavid A Winkler, Frank R Burden
Journal of Chemical Information and Modeling|July 10, 2015
Relevance Vector Machines: Sparse Classification Methods for QSARFrank R Burden, David A Winkler
Journal of Molecular Graphics & Modelling|May 10, 2005
Predictive Bayesian neural network models of MHC class II peptide bindingFrank R Burden, David A Winkler
Journal of Molecular Graphics & Modelling|June 9, 2004
Modelling blood-brain barrier partitioning using Bayesian neural netsDavid A Winkler, Frank R Burden
Molecular Biosystems|January 28, 2012
Robust, quantitative tools for modelling ex-vivo expansion of haematopoietic stem cells and progenitorsDavid A Winkler, Frank R Burden
Methods in Molecular Biology (Clifton, N.J.)|May 3, 2008
Nonlinear predictive modeling of MHC class II-peptide binding using Bayesian neural networksDavid A Winkler, Frank R Burden
Journal of Medicinal Chemistry|November 30, 2004
Broad-based quantitative structure-activity relationship modeling of potency and selectivity of farnesyltransferase inhibitors using a Bayesian regularized neural networkMitchell J Polley, David A Winkler, Frank R Burden
Journal of Chemical Information and Modeling|May 13, 2009
Toward novel universal descriptors: charge fingerprintsFrank R Burden, Mitchell J Polley, David A Winkler
Stem Cell Research|April 28, 2009
Stem cell decision making and critical-like exploratory networksJulianne D Halley, Frank R Burden, David A Winkler
Stem Cell Research|April 23, 2009
Toward a Rosetta stone for the stem cell genome: stochastic gene expression, network architecture, and external influencesJulianne D Halley, David A Winkler, Frank R Burden
Pageof 2