Bayesian versus Frequentist statistical modeling: a debate for hit selection from HTS campaigns.

[1]  Database resources of the National Center for Biotechnology Information , 2014, Nucleic Acids Res..

[2]  Jürgen Bajorath,et al.  Bayesian Screening for Active Compounds in High‐dimensional Chemical Spaces Combining Property Descriptors and Molecular Fingerprints , 2007, Chemical biology & drug design.

[3]  Chris L Waller,et al.  Strategies to support drug discovery through integration of systems and data. , 2007, Drug discovery today.

[4]  Andreas Bender,et al.  Understanding False Positives in Reporter Gene Assays: in Silico Chemogenomics Approaches To Prioritize Cell-Based HTS Data , 2007, J. Chem. Inf. Model..

[5]  Anthony E. Klon,et al.  Library Fingerprints: A Novel Approach to the Screening of Virtual Libraries , 2007, J. Chem. Inf. Model..

[6]  Herman van Vlijmen,et al.  Recent advances in chemoinformatics. , 2007, Journal of chemical information and modeling.

[7]  Paul Nightingale,et al.  The myth of the biotech revolution: An assessment of technological, clinical and organisational change , 2007 .

[8]  F. Sams-Dodd,et al.  Research & market strategy: how choice of drug discovery approach can affect market position. , 2007, Drug discovery today.

[9]  Andreas Bender,et al.  “Plate Cherry Picking”: A Novel Semi-Sequential Screening Paradigm for Cheaper, Faster, Information-Rich Compound Selection , 2007, Journal of biomolecular screening.

[10]  George Papadatos,et al.  Evaluation of machine-learning methods for ligand-based virtual screening , 2007, J. Comput. Aided Mol. Des..

[11]  Jürgen Bajorath,et al.  Towards Unified Compound Screening Strategies: A Critical Evaluation of Error Sources in Experimental and Virtual High‐Throughput Screening , 2006 .

[12]  Christopher P Austin,et al.  Measure, mine, model, and manipulate: the future for HTS and chemoinformatics? , 2006, Drug discovery today.

[13]  Esther F. Schmid,et al.  R&D technology investments: misguided and expensive or a better way to discover medicines? , 2006, Drug discovery today.

[14]  Meir Glick,et al.  Streamlining lead discovery by aligning in silico and high-throughput screening. , 2006, Current opinion in chemical biology.

[15]  Anthony E. Klon,et al.  Improved Naïve Bayesian Modeling of Numerical Data for Absorption, Distribution, Metabolism and Excretion (ADME) Property Prediction , 2006, J. Chem. Inf. Model..

[16]  F. Sams-Dodd,et al.  Drug discovery: selecting the optimal approach. , 2006, Drug discovery today.

[17]  Dariusz Plewczynski,et al.  Assessing Different Classification Methods for Virtual Screening , 2006, J. Chem. Inf. Model..

[18]  Meir Glick,et al.  Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases , 2006, J. Chem. Inf. Model..

[19]  R. Nadon,et al.  Inferential literacy for experimental high-throughput biology. , 2006, Trends in genetics : TIG.

[20]  Robert Nadon,et al.  Statistical practice in high-throughput screening data analysis , 2006, Nature Biotechnology.

[21]  Andrew Smellie,et al.  Surrogate docking: structure-based virtual screening at high throughput speed , 2005, J. Comput. Aided Mol. Des..

[22]  D. Rogers,et al.  Using Extended-Connectivity Fingerprints with Laplacian-Modified Bayesian Analysis in High-Throughput Screening Follow-Up , 2005, Journal of biomolecular screening.

[23]  V. Makarenkov,et al.  Statistical Analysis of Systematic Errors in High-Throughput Screening , 2005, Journal of biomolecular screening.

[24]  F. Sams-Dodd Target-based drug discovery: is something wrong? , 2005, Drug discovery today.

[25]  G. S. Gill,et al.  Molecular surface point environments for virtual screening and the elucidation of binding patterns (MOLPRINT) , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[26]  T. Insel,et al.  NIH Molecular Libraries Initiative , 2004, Science.

[27]  Paul Nightingale,et al.  The myth of the biotech revolution. , 2004, Trends in biotechnology.

[28]  David J Diller,et al.  Deriving knowledge through data mining high-throughput screening data. , 2004, Journal of medicinal chemistry.

[29]  Meir Glick,et al.  Application of Machine Learning To Improve the Results of High-Throughput Docking Against the HIV-1 Protease , 2004, J. Chem. Inf. Model..

[30]  Xiaoyang Xia,et al.  Classification of kinase inhibitors using a Bayesian model. , 2004, Journal of medicinal chemistry.

[31]  Anthony E. Klon,et al.  Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results. , 2004, Journal of medicinal chemistry.

[32]  Anthony E. Klon,et al.  Finding more needles in the haystack: A simple and efficient method for improving high-throughput docking results. , 2004, Journal of medicinal chemistry.

[33]  Meir Glick,et al.  Enrichment of Extremely Noisy High-Throughput Screening Data Using a Naïve Bayes Classifier , 2004, Journal of biomolecular screening.

[34]  Bert Gunter,et al.  Improved Statistical Methods for Hit Selection in High-Throughput Screening , 2003, Journal of biomolecular screening.

[35]  G. Rishton Nonleadlikeness and leadlikeness in biochemical screening. , 2003, Drug discovery today.

[36]  John D. Sterman,et al.  System Dynamics: Systems Thinking and Modeling for a Complex World , 2002 .

[37]  Paul Labute,et al.  A probabilistic approach to high throughput drug discovery. , 2002, Combinatorial chemistry & high throughput screening.

[38]  Michael I. Jordan,et al.  On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes , 2001, NIPS.

[39]  J H Zhang,et al.  Confirmation of primary active substances from high throughput screening of chemical and biological populations: a statistical approach and practical considerations. , 2000, Journal of combinatorial chemistry.

[40]  Thomas D. Y. Chung,et al.  A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays , 1999, Journal of biomolecular screening.

[41]  Paul Labute,et al.  Binary QSAR: A New Method for the Determination of Quantitative Structure Activity Relationships , 1998, Pacific Symposium on Biocomputing.

[42]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[43]  T. Bayes An essay towards solving a problem in the doctrine of chances , 2003 .

[44]  Meir Glick,et al.  Enrichment of High-Throughput Screening Data with Increasing Levels of Noise Using Support Vector Machines, Recursive Partitioning, and Laplacian-Modified Naive Bayesian Classifiers , 2006, J. Chem. Inf. Model..

[45]  Harry Zhang,et al.  The Optimality of Naive Bayes , 2004, FLAIRS.

[46]  Andreas Bender,et al.  Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier , 2004, J. Chem. Inf. Model..

[47]  Bradley Efron,et al.  Bayesians, Frequentists, and Physicists , 2003 .

[48]  W. Dunn,et al.  Principal components analysis and partial least squares regression , 1989 .