A systematic identification of multiple toxin-target interactions based on chemical, genomic and toxicological data.
暂无分享,去创建一个
Yan Li | Wei Zhou | Chao Huang | Yonghua Wang | Yan Li | Ling Yang | Chao Huang | J. Duan | Ling Yang | Yonghua Wang | Jinyou Duan | Wei Zhou
[1] Chih-Jen Lin,et al. A Practical Guide to Support Vector Classication , 2008 .
[2] Mitchell N. Cayen,et al. A guide to drug discovery: Making Better Drugs: Decision Gates in Non-Clinical Drug Development , 2003, Nature Reviews Drug Discovery.
[3] Igor V. Tetko,et al. Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis , 2008, J. Chem. Inf. Model..
[4] Heng Tao Shen,et al. Principal Component Analysis , 2009, Encyclopedia of Biometrics.
[5] D. Butina,et al. Predicting ADME properties in silico: methods and models. , 2002, Drug discovery today.
[6] Ruili Huang,et al. Weighted feature significance: a simple, interpretable model of compound toxicity based on the statistical enrichment of structural features. , 2009, Toxicological sciences : an official journal of the Society of Toxicology.
[7] Soyoung Lee,et al. Building a drug–target network and its applications , 2009, Expert opinion on drug discovery.
[8] A. Barabasi,et al. Lethality and centrality in protein networks , 2001, Nature.
[9] A. Bhalla,et al. Letter to the Editor: “Reply to “Comment on ‘Cytochrome-C Oxidase Inhibition in 26 Aluminum Phosphide Poisoned Patients’”” , 2007, Clinical toxicology.
[10] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[11] Yonghua Wang,et al. Probing the structural requirements of A-type Aurora kinase inhibitors using 3D-QSAR and molecular docking analysis , 2012, Journal of Molecular Modeling.
[12] T. Klabunde. Chemogenomic approaches to drug discovery: similar receptors bind similar ligands , 2007, British journal of pharmacology.
[13] Robert P. Sheridan,et al. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..
[14] Yan Li,et al. In Silico Prediction of Estrogen Receptor Subtype Binding Affinity and Selectivity Using Statistical Methods and Molecular Docking with 2-Arylnaphthalenes and 2-Arylquinolines , 2010, International journal of molecular sciences.
[15] R. Cooper,et al. The exposure to and health effects of antimony , 2009, Indian journal of occupational and environmental medicine.
[16] Fidel Ramírez,et al. Computing topological parameters of biological networks , 2008, Bioinform..
[17] B. Matthews,et al. Size versus polarizability in protein-ligand interactions: binding of noble gases within engineered cavities in phage T4 lysozyme. , 2000, Journal of molecular biology.
[18] Yue Yu,et al. In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods. , 2011, Chemosphere.
[19] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[20] P. Imming,et al. Drugs, their targets and the nature and number of drug targets , 2006, Nature Reviews Drug Discovery.
[21] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[22] Trey Ideker,et al. Cytoscape 2.8: new features for data integration and network visualization , 2010, Bioinform..
[23] H. Mewes,et al. Can we estimate the accuracy of ADME-Tox predictions? , 2006, Drug discovery today.
[24] K. Groundstroem,et al. Exposure to cobalt in the production of cobalt and cobalt compounds and its effect on the heart , 2004, Occupational and Environmental Medicine.
[25] A. Viarengo,et al. Effects of heavy metals on phospholipase C in gill and digestive gland of the marine mussel Mytilus galloprovincialis Lam. , 2000, Comparative biochemistry and physiology. Part B, Biochemistry & molecular biology.
[26] Keld Kjeldsen,et al. The Na, K-ATPase in the failing human heart. , 2003, Cardiovascular research.
[27] H. Elsenbeer,et al. Soil organic carbon concentrations and stocks on Barro Colorado Island — Digital soil mapping using Random Forests analysis , 2008 .
[28] Y. Li,et al. Estimation of bioconcentration factors using molecular electro-topological state and flexibility , 2008, SAR and QSAR in environmental research.
[29] H. Yabuuchi,et al. Analysis of multiple compound–protein interactions reveals novel bioactive molecules , 2011, Molecular systems biology.
[30] Jun Dong,et al. Understanding network concepts in modules , 2007, BMC Systems Biology.
[31] Yoshihiro Yamanishi,et al. Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework , 2010, Bioinform..
[32] Giovanni Scardoni,et al. Analyzing biological network parameters with CentiScaPe , 2009, Bioinform..
[33] Yoshihiro Yamanishi,et al. Prediction of drug–target interaction networks from the integration of chemical and genomic spaces , 2008, ISMB.
[34] Kai Wang,et al. Support Vector Machine-Based Feature Selection for Classification of Liver Fibrosis Grade in Chronic Hepatitis C , 2006, Journal of Medical Systems.
[35] Stefan Rüping,et al. A Simple Method For Estimating Conditional Probabilities For SVMs , 2004, LWA.
[36] Jiangyong Gu,et al. Computational pharmacological studies on cardiovascular disease by Qishen Yiqi Diwan , 2009 .
[37] Hua Yu,et al. A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data , 2012, PloS one.
[38] R N Ratnaike,et al. Acute and chronic arsenic toxicity , 2003, Postgraduate medical journal.