Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets

[1]  Sean Ekins,et al.  Validating New Tuberculosis Computational Models with Public Whole Cell Screening Aerobic Activity Datasets , 2011, Pharmaceutical Research.

[2]  Joel S. Freundlich,et al.  Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery. , 2011, Trends in Microbiology.

[3]  Sean Ekins,et al.  Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis. , 2010, Molecular bioSystems.

[4]  Robert C. Glen,et al.  Predicting Phospholipidosis Using Machine Learning , 2010, Molecular pharmaceutics.

[5]  Kenji Mizuguchi,et al.  Applying the Naïve Bayes classifier with kernel density estimation to the prediction of protein-protein interaction sites , 2010, Bioinform..

[6]  Sean Ekins,et al.  A collaborative database and computational models for tuberculosis drug discovery. , 2010, Molecular bioSystems.

[7]  Ian H. Witten,et al.  WEKA - Experiences with a Java Open-Source Project , 2010, J. Mach. Learn. Res..

[8]  Amanda C. Schierz Virtual screening of bioassay data , 2009, J. Cheminformatics.

[9]  Lynn Rasmussen,et al.  Antituberculosis activity of the molecular libraries screening center network library. , 2009, Tuberculosis.

[10]  Lynn Rasmussen,et al.  High-throughput screening for inhibitors of Mycobacterium tuberculosis H37Rv. , 2009, Tuberculosis.

[11]  Yanli Wang,et al.  PubChem: a public information system for analyzing bioactivities of small molecules , 2009, Nucleic Acids Res..

[12]  Jonathan D Hirst,et al.  Machine learning in virtual screening. , 2009, Combinatorial chemistry & high throughput screening.

[13]  Olivier Taboureau,et al.  Classification of Cytochrome P450 1A2 Inhibitors and Noninhibitors by Machine Learning Techniques , 2009, Drug Metabolism and Disposition.

[14]  Ovidiu Ivanciuc,et al.  Weka machine learning for predicting the phospholipidosis inducing potential. , 2008, Current topics in medicinal chemistry.

[15]  Jean-Philippe Vert,et al.  Machine Learning for In Silico Virtual Screening and Chemical Genomics: New Strategies , 2008, Combinatorial chemistry & high throughput screening.

[16]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[17]  R. Reynolds,et al.  Programs to facilitate tuberculosis drug discovery: the tuberculosis antimicrobial acquisition and coordinating facility. , 2007, Infectious disorders drug targets.

[18]  Victor S. Sheng,et al.  Thresholding for Making Classifiers Cost-sensitive , 2006, AAAI.

[19]  Jun Feng,et al.  PowerMV: A Software Environment for Molecular Viewing, Descriptor Generation, Data Analysis and Hit Evaluation , 2005, J. Chem. Inf. Model..

[20]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[21]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[22]  Charles Elkan,et al.  The Foundations of Cost-Sensitive Learning , 2001, IJCAI.

[23]  Tim D. J. Perkins,et al.  Large-scale virtual screening for discovering leads in the postgenomic era , 2001, IBM Syst. J..

[24]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[25]  Lahana,et al.  How many leads from HTS? , 1999, Drug discovery today.

[26]  Pedro M. Domingos MetaCost: a general method for making classifiers cost-sensitive , 1999, KDD '99.

[27]  J. Platt Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[28]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[29]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[30]  M. Iseman,et al.  Evolution of drug-resistant tuberculosis: a tale of two species. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[31]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[32]  R. Reynolds,et al.  High Throughput Screening for Inhibitors of Mycobacterium tuberculosis H 37 Rv , 2012 .

[33]  鲁晶晶(编译),et al.  BMC Research Notes将免费提供更多黑色数据 , 2010 .

[34]  Olivier Taboureau,et al.  Classification of Cytochrome P450 1A2 Inhibitors and Noninhibitors by Machine Learning Techniques , 2009, Drug Metabolism and Disposition.

[35]  Nathalie Japkowicz,et al.  The Class Imbalance Problem: Significance and Strategies , 2000 .