Ying Xue

发表

Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods.

Z. R. Li, Y. Z. Chen, C. Ung, 2006, Journal of molecular graphics & modelling.

Quantitative structure–activity relationship study of influenza virus neuraminidase A/PR/8/34 (H1N1) inhibitors by genetic algorithm feature selection and support vector regression

Ying Xue, Xue-Gang Yang, Bing-Ke Li, 2013 .

Modeling, predicting and virtual screening of selective inhibitors of MMP-3 and MMP-9 over MMP-1 using random forest classification

Yu Zong Chen, Bing He, Bing-Ke Li, 2015 .

Prediction of antibacterial compounds by machine learning approaches

Min Wang, Duan Chen, Yu Zong Chen, 2009, J. Comput. Chem..

Prediction of matrix metal proteinases-12 inhibitors by machine learning approaches

Min Yang, Li Hu, Ying Xue, 2018, Journal of biomolecular structure & dynamics.

Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic, or toxicological properties of pharmaceutical agents

Ying Xue, Lianyi Han, Yu Zong Chen, 2005 .

A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor.

Z. R. Li, Y. Z. Chen, Z. Cao, 2008, Journal of molecular graphics & modelling.

Effect of Selection of Molecular Descriptors on the Prediction of Blood-Brain Barrier Penetrating and Nonpenetrating Agents by Statistical Learning Methods

Zhi-Wei Cao, Yu Zong Chen, Ying Xue, 2005, J. Chem. Inf. Model..

Identification of vasodilators from molecular descriptors by machine learning methods

Ying Xue, Xue-Gang Yang, Yong Cong, 2010 .

Prediction of acetylcholinesterase inhibitors and characterization of correlative molecular descriptors by machine learning methods.

Wei-Ke Lv, Y. Xue, Ying Xue, 2010, European journal of medicinal chemistry.

Prediction of genotoxicity of chemical compounds by statistical learning methods.

Z R Li, Z. R. Li, Y Xue, 2005, Chemical research in toxicology.

Prediction of P-Glycoprotein Substrates by a Support Vector Machine Approach

J. F. Wang, Zhi-Wei Cao, Yu Zong Chen, 2004, J. Chem. Inf. Model..

Evaluation of Virtual Screening Performance of Support Vector Machines Trained by Sparsely Distributed Active Compounds

Boon Chuan Low, Yu Zong Chen, Ying Xue, 2008, J. Chem. Inf. Model..

Prediction of torsade-causing potential of drugs by support vector machine approach.

Y Xue, C W Yap, Y Z Chen, 2004, Toxicological sciences : an official journal of the Society of Toxicology.

Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins.

Z R Li, Z. R. Li, C W Yap, 2007, Journal of pharmaceutical sciences.

Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents

Xin Chen, Ze-Rong Li, Yu Zong Chen, 2004, J. Chem. Inf. Model..

In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method

Ying Xue, Xue-Gang Yang, Yong Cong, 2013, Comput. Biol. Medicine.

Prediction of factor Xa inhibitors by machine learning methods.

H H Lin, L Y Han, Y Xue, 2007, Journal of molecular graphics & modelling.

Effect of training data size and noise level on support vector machines virtual screening of genotoxic compounds from large compound libraries

Jia Jia, Ze-Rong Li, Bu-Cong Han, 2011, J. Comput. Aided Mol. Des..