John B. O. Mitchell
Orcid: 0000-0002-0379-6097
According to our database1,
John B. O. Mitchell
authored at least 44 papers
between 1994 and 2022.
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Bibliography
2022
Practical application of a Bayesian network approach to poultry epigenetics and stress.
BMC Bioinform., 2022
2017
2016
J. Chem. Inf. Model., 2016
2015
Pattern Recognit. Lett., 2015
J. Cheminformatics, 2015
Predicting targets of compounds against neurological diseases using cheminformatic methodology.
J. Comput. Aided Mol. Des., 2015
2014
Source Code Biol. Medicine, 2014
Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules.
J. Chem. Inf. Model., 2014
Erratum for "In Silico Target Predictions: Defining a Benchmarking Data Set and Comparison of Performance of the Multiclass Naı̈ve Bayes and Parzen-Rosenblatt Window".
J. Chem. Inf. Model., 2014
BMC Bioinform., 2014
2013
In Silico Target Predictions: Defining a Benchmarking Data Set and Comparison of Performance of the Multiclass Naïve Bayes and Parzen-Rosenblatt Window.
J. Chem. Inf. Model., 2013
J. Cheminformatics, 2013
2012
Winnow based identification of potent hERG inhibitors in silico: comparative assessment on different datasets.
J. Cheminformatics, 2012
2011
J. Chem. Inf. Model., 2011
Comments on "Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets": Significance for the Validation of Scoring Functions.
J. Chem. Inf. Model., 2011
2010
Quantitative Comparison of Catalytic Mechanisms and Overall Reactions in Convergently Evolved Enzymes: Implications for Classification of Enzyme Function.
PLoS Comput. Biol., 2010
A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking.
Bioinform., 2010
2008
How To Winnow Actives from Inactives: Introducing Molecular Orthogonal Sparse Bigrams (MOSBs) and Multiclass Winnow.
J. Chem. Inf. Model., 2008
Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance Statistics.
J. Chem. Inf. Model., 2008
Why Are Some Properties More Difficult To Predict than Others? A Study of QSPR Models of Solubility, Melting Point, and Log P.
J. Chem. Inf. Model., 2008
2007
MACiE (Mechanism, Annotation and Classification in Enzymes): novel tools for searching catalytic mechanisms.
Nucleic Acids Res., 2007
Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds.
J. Comput. Aided Mol. Des., 2007
2006
Melting Point Prediction Employing <i>k</i>-Nearest Neighbor Algorithms and Genetic Parameter Optimization.
J. Chem. Inf. Model., 2006
Chemoinformatics-Based Classification of Prohibited Substances Employed for Doping in Sport.
J. Chem. Inf. Model., 2006
Classifying the World Anti-Doping Agency's 2005 Prohibited List Using the Chemistry Development Kit Fingerprint.
Proceedings of the Computational Life Sciences II, 2006
2005
2003
Protein Ligand Database (PLD): additional understanding of the nature and specificity of protein-ligand complexes.
Bioinform., 2003
2001
The Relationship between the Sequence Identities of Alpha Helical Proteins in the PDB and the Molecular Similarities of Their Ligands.
J. Chem. Inf. Comput. Sci., 2001
Evaluation of a knowledge-based potential of mean force for scoring docked protein-ligand complexes.
J. Comput. Chem., 2001
1999
BLEEP - potential of mean force describing protein-ligand interactions: I. Generating potential.
J. Comput. Chem., 1999
BLEEP - potential of mean force describing protein-ligand interactions: II. Calculation of binding energies and comparison with experimental data.
J. Comput. Chem., 1999
1994
Gaussian Multipoles in Practice: Electrostatic Energeis for Intermolecular Potentials.
J. Comput. Chem., 1994