Qing-Song Xu

Current Position:

Professor inStatistics.  

Coordinates:

Department of Statistics  

School of Mathematics & Statistics  

Central South University  

EMAIL:qsxu@csu.edu.cn

Education :

lPh.D. in Science, Hunan University, P.R.China  

lM.Sc. in Science, Hunan University, P.R.China  

lB. Sc. In Engineering, Hefei University of Technology, P.R.China  

Professionalexperiences:

l2004-current: Professor, School of Mathematics & Statistics, CentralSouth  

University.  

l10/2013-09/2013, Research Follow, AppliedBiology & Chemistry, The Hong Kong  

PolytechnicUniversity  

l11/2011-08/2011, Research Follow, AppliedBiology & Chemistry, The Hong Kong  

PolytechnicUniversity  

l05/2002-08/2004, Research Follow,Department of Analytical Chemistry and

PharmaceuticalTechnology,Vrije Universiteit Brussel.

l1999-2002: Associate professor, College of Mathematics &Econometrics, Hunan University  

l1989-1998: Assistant professor, Department ofApplied Mathematics, Hunan University.  

l01/1999 -04/1999: Visiting Scholar, Statistics Research andConsultancy Centre,  

Hong KongBaptist University.  

Current research interests:

lHigh Dimensional Data Analysis;  

lStatistical Learning Method in Biostatistics andChemometrics;  

Teaching

Calculus;Probability and Statistics; Regression Analysis; Statistical Learning  

 

Selected Publications:

1Xu, J, Xu, Q.S., Chan, C.O., Mok, D.K.W., Yi, L.Z., Chau,F.T., Identifying bioactive components innatural products through chromatographic fingerprint, Analytica chimica acta870, 45-55, 2015  

2Xiao,N., Xu, Q.S.,Multi-step adaptiveelastic-net: reducing false positives in high-dimensional variable selectionJournal of Statistical Computation and Simulation, 1-11  

3Xiao,N., Xu, Q.S., Cao, D.S. protr: Rpackage for generating various numerical representation schemes of proteinsequence, Bioinformatics, btv042, 2015.  

4Cao, D.S., Xiao, N., Xu, Q.S., Chen, A.F., Rcpi:R/Bioconductor package to generate various descriptors of proteins, compounds,and their interactions, Bioinformatics, btu624, 2014  

5Huang, X., Cao, D.-S.,Xu,Q.-S.*, and Liang, Y.-Z., 'A Novel Tree Kernel Partial Least Squares forModeling the Structure-Activity Relationship',Journal of Chemometrics,27 (2013), 43-49.

6Cao, D.-S.,Xu, Q.-S.,and Liang, Y.-Z., 'Propy: A Tool to Generate Various Modes of Chou's Pseaac',Bioinformatics,29 (2013), 960-62.

7Huang, X., Cao, D.S.,Xu, Q. S.*, Shen, L., Huang, J. H., and Liang, Y. Z., 'A Novel TreeKernel Support Vector Machine Classifier for Modeling the Relationship betweenBioactivity and Molecular Descriptors',Chemometrics and IntelligentLaboratory Systems,120 (2013), 71-76.

8Li, H.-D.,Xu, Q.-S.,Zhang, W., and Liang, Y.-Z. (2012). Variable complementary network: a novelapproach for identifying biomarkers and their mutual associations. Metabolomics8, 1218-1226.

9Li, H.-D.,Xu, Q.-S.,and Liang, Y.-Z. (2012). Random frog: An efficient reversible jump Markov ChainMonte Carlo-like approach for variable selection with applications to geneselection and disease classification. Analytica Chimica Acta 740, 20-26.

10Fu, G.-H., andXu,Q.-S. (2012). Grouping Variable Selection by Weight Fused Elastic Net forMulti-Collinear Data. Communications in Statistics-Simulation and Computation41, 205-221.

11Cao, D.-S.,Xu, Q.-S.,Zhang, L.-X., Huang, J.-H., and Liang, Y.-Z. (2012). Tree-based ensemblemethods and their applications in analytical chemistry. Trac-Trends inAnalytical Chemistry 40, 158-167.

12Fu, G.-H.,Xu, Q.-S.,Li, H.-D., Cao, D.-S., and Liang, Y.-Z. (2011). Elastic Net Grouping VariableSelection Combined with Partial Least Squares Regression (EN-PLSR) for theAnalysis of Strongly Multi-collinear Spectroscopic Data. Applied Spectroscopy65, 402-408.

13Fu, G.-H., Cao, D.-S.,Xu,Q.-S., Li, H.-D., and Liang, Y.-Z. (2011). Combination of kernel PCA andlinear support vector machine for modeling a nonlinear relationship betweenbioactivity and molecular descriptors. Journal of Chemometrics 25, 92-99.

14Cao, D., Liang, Y., Xu,Q., Yun, Y., and Li, H. (2011). Toward better QSAR/QSPR modeling: simultaneousoutlier detection and variable selection using distribution of model features.Journal of Computer-Aided Molecular Design 25, 67-80.  

15Haupt, B., Schwartz,M.R., Xu, Q., and Ro, J.Y. (2010). Columnar cell lesions: a consensus studyamong pathology trainees. Human Pathology 41, 895-901.  

16Cao, D.-S., Liang,Y.-Z.,Xu, Q.-S., Li, H.-D., and Chen, X. (2010). A New Strategy ofOutlier Detection for QSAR/QSPR. Journal of Computational Chemistry 31,592-602.

17Liang, Y., Yi, L., andXu, Q. (2008). Chemometrics and modernization of traditional Chinese medicine.Science in China Series B-Chemistry 51, 718-728.  

18Xu, Q.S., Daeyaert, F.,Lewi, P.J., and Massart, D.L. (2006). Studies of relationship betweenbiological activities and HIV reverse transcriptase inhibitors by multivariateadaptive regression splines with curds and whey. Chemometrics and IntelligentLaboratory Systems 82, 24-30.  

19Zhang, M.H., Xu, Q.S.,and Massart, D.L. (2005). Boosting partial least squares. Analytical Chemistry77, 1423-1431.  

20Xu, Q.S., Liang, Y.Z., and Hou,Z.T. (2005). A multi-sequential number-theoretic optimization algorithm usingclustering methods. Journal of Central South University of Technology 12,283-293.

21Deconinck, E., Xu, Q.S.,Put, R., Coomans, D., Massart, D.L., and Heyden, Y.V. (2005). Prediction ofgastro-intestinal absorption using multivariate adaptive regression splines.Journal of Pharmaceutical and Biomedical Analysis 39, 1021-1030.  

22Xu, Q.S., Liang, Y.Z.,and Du, Y.P. (2004). Monte Carlo cross-validation for selecting a model andestimating the prediction error in multivariate calibration. Journal ofChemometrics 18, 112-120.  

23Xu, Q.S., de Jong, S.,Lewi, P., and Massart, D.L. (2004). Partial least squares regression with Curdsand Whey. Chemometrics and Intelligent Laboratory Systems 71, 21-31.  

24Xu, Q.S., Daszykowski,M., Walczak, B., Daeyaert, F., de Jonge, M.R., Heeres, J., Koymans, L.M.H.,Lewi, P.J., Vinkers, H.M., Janssen, P.A., et al. (2004). Multivariate adaptiveregression splines - studies of HIV reverse transcriptase inhibitors.Chemometrics and Intelligent Laboratory Systems 72, 27-34.  

25Put, R., Xu, Q.S.,Massart, D.L., and Heyden, Y.V. (2004). Multivariate adaptive regressionsplines (MARS) in chromatographic quantitative structure-retention relationshipstudies. Journal of Chromatography A 1055, 11-19.  

26Xu, Q.S., Massart, D.L.,Liang, Y.Z., and Fang, K.T. (2003). Two-step multivariate adaptive regressionsplines for modeling a quantitative relationship between gas chromatographyretention indices and molecular descriptors. Journal of Chromatography A 998,155-167.  

27Xu, Q.S., Liang, Y.Z.,and Shen, H.L. (2001). Generalized PLS regression. Journal of Chemometrics 15,135-148.  

28Xu, Q.S., and Liang,Y.Z. (2001). Monte Carlo cross validation. Chemometrics and IntelligentLaboratory Systems 56, 1-11.  

29Xu, Q.S., Liang, Y.Z.,and Fang, K.T. (2000). The effects of different experimental designs onparameter estimation in the kinetics of a reversible chemical reaction.Chemometrics and Intelligent Laboratory Systems 52, 155-166.  

30Xu, Q.S., and Liang,Y.Z. (1999). On the equivalence of window factor analysis and orthogonalprojection resolution. Chemometrics and Intelligent Laboratory Systems 45,335-338.  

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