Journal Publications

• [21]Liang Y-Z, Lu H-M, Yang R-H, Yun Y-H, Cao D-S, Deng B-C, Wen M.The model adaptive space shrinkage (MASS) approach: a new method for simultaneous variable selection and outlier detection based on model population analysis[J].Analyst, 2016, 141: 5586-5597.
• [22]Liang Y-z, Yi L-z, Ji H-c, huang J-h, Lu H-M, Dai L, Gon？alves C, Lin Z.Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest algorithm[J].Analytica Chimica Acta, 2014, 827: 22-27.
• [23]Liang Y-Z, Chen X-Q, Lu H-M, Zhang M-J, Ma P, Peng Y, Tong X, Zhang Z-M.Multiscale peak detection in wavelet space[J].Analyst, 2015, 140: 7955 - 7964.
• [24]Liang Y-Z, Luo Q-Y, Lu H-M, Wang W-T, Yin Y-L, Cao D-S, Yun Y-H, Deng B-C.A bootstrapping soft shrinkage approach for variable selection in chemical modeling.Analytica Chimica Acta, 2016, 908: 63-74.
• [25]Xu Q-S, Lu H-M, Cao D-S, Li H-D, Liang Y, Tan M-L, Wang W-T, Yun Y-H.A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration[J].Analytica Chimica Acta, 2014, 807: 36-43.
• [26]Liang Y, Lu H, Zeng M, Tan B, Zhang L.Establishment of reliable mass spectra and retention indices library: identification of fatty acids in human plasma without authentic standards[J].Talanta, 2012, 88: 311-317.
• [27]Ferro M, Xu X-N, Tan B-B, Lu H-M, Liang Y-Z, Zhang Z-M.Multiscale peak alignment for chromatographic datasets[J].Journal of Chromatography, 2012, 1223: 93–106.
• [28]Liang Y, Xu Q, Lu H, Ferro MD, Wu Q, Yan J, Huang J.Selective of informative metabolites using random forests based on model population analysis[J].Talanta, 2013, 117: 549-555.
• [29]Liang Y-Z, Xu Q-S, Lu H, Yan J, Xie H-L, Huang J.Using random forest to classify T-cell epitopes based on amino acid properties and molecular features[J].Analytica Chimica Acta, 2013, 804: :70-75.
• [30]Lu H-M, Liang Y-Z, Fu L, Xie H-L, Tang L-J, Wen M, Huang J-H.Using random forest to classify linear B-cell epitopes based on amino acid properties and molecular features[J].Biochimie, 2014, 103C: 1-6.
• [31]Liang Y, Kell DB, Shen H, Dunn WB, Lu H.Comparative evaluation of software for the deconvolution of metabolomics data based on GC-TOF-MS[J].Trends in Analytical Chemistry, 2008, 27: 215-227.
• [32]Lu H, Liang Y, Yun Y, Zhou X, Zhang Z, Ma P.Feature extraction from resolution perspective for gas chromatography-mass spectrometry datasets[J].RSC Advances, 2016, 6: 113997 - 114004.
• [33]Liang Y-Z:, Hu Q-N, Huang J-H, Lu H-M, Xu Q-S, Liu S, Cao D-S.Large-scale prediction of drug–target interactions using protein sequences and drug topological structures[J].Analytica Chimica Acta, 2012, 752: 1-10.
• [34]Liang Y, Yi L, Jin Y, Lu H, Zhang Z, Lin Z.Joint MS-based platforms for comprehensive comparison of rat plasma and serum metabolic profiling[J].Biomedical Chromatography, 2014, 28: 1235-1245.
• [35]Liang Y-Z, Lu H-M, Zhang L-X, Hu Q-N, Xu Q-S, Wang B, Yi L-Z, Zeng M-M, Cao D-S.A novel kernel Fisher discriminant analysis: Constructing informative kernel by decision tree ensemble for metabolomics data analysis[J].Analytica Chimica Acta, 2011, 706: 97-104.
• [36]Zhan D, Liang Y, Lu H, Zhang Z-M, Wen M, Zhang M.Robust alignment of chromatograms bystatistically analyzing shifts matrix generated by moving window fast Fourier transform cross-correlation[J].Journal of Separation Science, 2017, 38: 965–974.
• [37]Zhang ZM, Lu HM, Ji HC.Pure ion chromatogram extraction via optimal k-means clustering[J].RSC Advances, 2016, 6: S6977-S6985.
• [38]Liang Y, Zhang Z, Gan D, Lu H.Sample classification of GC-ToF-MS metabolomics data without the requirement for chromatographic deconvolution[J].Metabolomics, 2010, 7: 191-205.
• [39]Liang Y-Z, Xu Q-S, Lu H-M, Cao D-S, Yan J, Xie H-L, Huang J-H.Interpretation of type 2 diabetes mellitus relevant GC-MS metabolomics fingerprints by using random forests[J].Analytical Methods, 2013, 5: 4883-4889.
• [40]Yang Z-Y, Ye F, Zhou J, Xu D, Fan W, Liang Y-Z, Lu H-M, Chen X-Q, Zhang Z-M.Mixture analysis using reverse searching and non-negative least squares[J].Chemometrics and Intelligent Laboratory Systems, 2014, 137: 10-20.