研究成果

2020

  1. Yang, Z.; He, J.; Lu, A.; Hou, T.; Cao, D., The Application of Negative Design to Design More Desirable Virtual Screening Library. J MED CHEM 2020,.[PDF]
  2. Yang, Z.; He, J.; Lu, A.; Hou, T.; Cao, D., Frequent hitters: nuisance artifacts in high-throughput screening. DRUG DISCOV TODAY 2020,.[PDF]
  3. Shen, C.; Hu, Y.; Wang, Z.; Zhang, X.; Zhong, H.; Wang, G.; Yao, X.; Xu, L.; Cao, D.; Hou, T., Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions. BRIEF BIOINFORM 2020,.[PDF]
  4. Wang, Z.; Sun, H.; Shen, C.; Hu, X.; Gao, J.; Li, D.; Cao, D.; Hou, T., Combined Strategies in Structure-based Virtual Screening. PHYS CHEM CHEM PHYS 2020,.[PDF]
  5. Wang, Z.; Wang, X.; Kang, Y.; Zhong, H.; Shen, C.; Yao, X.; Cao, D.; Hou, T., Binding affinity and dissociation pathway predictions for a series of USP7 inhibitors with pyrimidinone scaffold by multiple computational methods. PHYS CHEM CHEM PHYS 2020,.[PDF]
  6. Shen, C.; Ding, J.; Wang, Z.; Cao, D.; Ding, X.; Hou, T., From machine learning to deep learning: Advances in scoring functions for protein-ligand docking. Wiley Interdisciplinary Reviews: Computational Molecular Science 2020, 10, e1429.[PDF]
  7. Jiang, D.; Lei, T.; Wang, Z.; Shen, C.; Cao, D.; Hou, T., ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning. J CHEMINFORMATICS 2020, 12, 1-26.[PDF]

2019

  1. Yang, Z.; Yang, Z.; Dong, J.; Wang, L.; Zhang, L.; Ding, J.; Ding, X.; Lu, A.; Hou, T.; Cao, D., Structural Analysis and Identification of Colloidal Aggregators in Drug Discovery. J CHEM INF MODEL 2019, 59, 3714-3726.[PDF]
  2. Wen, M.; Deng, Z.; Jiang, S.; Guan, Y.; Wu, H.; Wang, X.; Xiao, S.; Zhang, Y.; Yang, J.; Cao, D., Identification of a novel Bcl-2 inhibitor by ligand-based screening and investigation of its anti-Cancer effect on human breast Cancer cells. FRONT PHARMACOL 2019, 10, 391.[PDF]
  3. Yun, Y.; Li, H.; Deng, B.; Cao, D., An overview of variable selection methods in multivariate analysis of near-infrared spectra. TrAC Trends in Analytical Chemistry 2019,.[PDF]
  4. Ye, W.; Yang, S.; Zhang, L.; Deng, Z.; Li, W.; Zhang, J.; Zhang, L.; Yun, Y.; Chen, A. F.; Cao, D., Multistep virtual screening for rapid identification of G Protein-Coupled Receptors Kinase 2 inhibitors for heart failure treatment. CHEMOMETR INTELL LAB 2019, 185, 32-40.[PDF]
  5. Jiang, S.; Chen, X.; Ge, P.; Wang, X.; Lao, Y.; Xiao, S.; Zhang, Y.; Yang, J.; Xu, X.; Cao, D., Tubeimoside-1, a triterpenoid saponin, induces cytoprotective autophagy in human breast cancer cells in vitro via Akt-mediated pathway. ACTA PHARMACOL SIN 2019, 40, 919-928.[PDF]
  6. Fu, L.; Liu, L.; Yang, Z.; Pan, L.; Ding, J.; Yun, Y.; Lu, A.; Hou, T.; Cao, D., Systematic Modeling of logD7. 4 Based on Ensemble Machine Learning, Group Contribution and Matched Molecular Pair Analysis. J CHEM INF MODEL 2019,.[PDF]
  7. Ye, W.; Zhang, L.; Guan, Y.; Xue, W.; Chen, A. F.; Cao, Q.; Cheng, Y.; Cao, D., Virtual screening and experimental validation of eEF2K inhibitors by combining homology modeling, QSAR and molecular docking from FDA approved drugs. NEW J CHEM 2019, 43, 19097-19106.[PDF]
  8. Dong, J., Zhu, M. F., Yun, Y. H., Lu, A. P., Hou, T. J., & Cao, D. S., BioMedR: an R/CRAN package for integrated data analysis pipeline in biomedical study. Briefings in Bioinformatics. 2019,.[PDF]
  9. Jiang, S.; Chen, X.; Ge, P.; Wang, X.; Lao, Y.; Xiao, S.; Zhang, Y.; Yang, J.; Xu, X.; Cao, D., Tubeimoside-1, a triterpenoid saponin, induces cytoprotective autophagy in human breast cancer cells in vitro via Akt-mediated pathway. ACTA PHARMACOL SIN 2019, 40, 919-928.[PDF]

2018

  1. Liu, L.; Fu, L.; Zhang, J.; Wei, H.; Ye, W.; Deng, Z.; Zhang, L.; Cheng, Y.; Ouyang, D.; Cao, Q., Three-Level Hepatotoxicity Prediction System Based on Adverse Hepatic Effects. MOL PHARMACEUT 2018, 16, 393-408.[PDF]
  2. Tang, Y.; Zhang, W.; Zhu, M.; Zheng, L.; Xie, L.; Yao, Z.; Zhang, H.; Cao, D.; Lu, B., Lupus nephritis pathology prediction with clinical indices. SCI REP-UK 2018, 8, 1-8.[PDF]
  3. Dong, J.; Wang, N.; Yao, Z.; Zhang, L.; Cheng, Y.; Ouyang, D.; Lu, A.; Cao, D., ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. J CHEMINFORMATICS 2018, 10, 29.[PDF]
  4. Dong, J.; Yao, Z.; Zhang, L.; Luo, F.; Lin, Q.; Lu, A.; Chen, A. F.; Cao, D., PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions. J CHEMINFORMATICS 2018, 10, 16.[PDF]
  5. Wang, N.; Dong, J.; Zhang, L.; Ouyang, D.; Cheng, Y.; Chen, A. F.; Lu, A.; Cao, D., HAMdb: a database of human autophagy modulators with specific pathway and disease information. J CHEMINFORMATICS 2018, 10, 1-8.[PDF]

2017

  1. Yin-Hua Deng, Ning-Ning Wang, Zhen-Xing Zou, Lin Zhang, Kang-Ping Xu, Alex F. Chen, Dong-Sheng Cao and Gui-Shan Tan, Multi-Target Screening and Experimental Validation of Natural Products from Selaginella Plants against Alzheimer's Disease, Front. Pharmacol., 8:539.[PDF]
  2. Jie Dong, Zhi-Jiang Yao, Min-Feng Zhu, Ning-Ning Wang, Ben Lu, Alex F Chen, Ai-Ping Lu, Hongyu Miao, Wen-Bin Zeng, Dong-Sheng Cao. ChemSAR: an online pipelining platform for molecular SAR modeling. Journal of Cheminformatics. 2017, 9(1): 27. [PDF] 
  3. Lin Zhang Tao Yang,Fang Zhu,Tianxiao Zhou,Dong-Sheng Cao,Qinlu Lin. Label-free, Water-soluble Fluorescent Peptide Probe for a Sensitive and Selective Determination of Copper Ions. Analytical Sciences. 2017, 33(2): 191-196. [PDF] 
  4. Gui-Shan Tan Zhen-Xing Zou,Kang-Ping Xu,Ping-Sheng Xu,Xia Yu,Dong-Sheng Cao. Seladoeflavones A-F, six novel flavonoids from Selaginella doederleinii. Fitoterapia.  2017, 116: 66-71. [PDF] 
  5. Gui-Shan Tan Zhen-Xing Zou,Ping-Sheng Xu,Guogang Zhang,Dong-Sheng Cao,Kang-Ping Xu. Selagintriflavonoids with BACE1 inhibitory activity from the fern Selaginella doederleinii. Phytochemistry. 2017, 134: 114-121. [PDF] 
  6. Ning-Ning Wang, Chen Huang, Jie Dong, Zhi-Jiang Yao, Min-Feng Zhu, Zhen-Ke Deng, Ben Lv, Ai-Ping Lu, Alex F Chen, Dong-Sheng Cao. Predicting human intestinal absorption with modified random forest approach: a comprehensive evaluation of molecular representation, unbalanced data, and applicability domain issues. RSC Advances. 2017, 7(31): 19007-19018. [PDF] 

2016

  1. Jie Dong, Zhi-Jiang Yao, Ming Wen, Min-Feng Zhu, Ning-Ning Wang, Hong-Yu Miao, Ai-Ping Lu, Wen-Bin Zeng and Dong-Sheng Cao.BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions. Journal of Cheminformatics 2016, 8:34 [PDF]
  2. NingNing Wang, Jie Dong, YinHua Deng, et al. ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting. Journal of Chemical Information and Modeling. 2016, 56 (4), pp 763–773 [PDF]
  3. Zhi-Jiang Yao , Jie Dong , Yu-Jing Che , Min-Feng Zhu, Ming Wen, Ning-Ning Wang, Shan Wang, Ai-Ping Lu, Dong-Sheng Cao. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models. Journal of Computer-Aided Molecular Design, 2016 May 11. [PDF] 
  4. Yizeng Liang, Qing-Song Xu, Hong-Dong Li, Dong-Sheng Cao. Support vector machines and their application in chemistry and biotechnology. CRC Press, 2016.4.19. [PDF]
  5. Qing-Song Xu, Jian Xu, Dong-Sheng Cao, Yi-Zeng Liang. Boosting in block variable subspaces: An approach of additive modeling for structure–activity relationship.  Chemometrics and Intelligent Laboratory Systems. 2016, 152: 134-139. [PDF] 
  6. Yong-Huan Yun, Bai-Chuan Deng, Dong-Sheng Cao, Wei-Ting Wang, Yi-Zeng Liang. Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery. Analytica chimica acta. 2016, 911: 27-34. [PDF] 
  7. Bai-Chuan Deng, Yong-Huan Yun, Dong-Sheng Cao, Yu-Long Yin, Wei-Ting Wang, Hong-Mei Lu, Qian-Yi Luo, Yi-Zeng Liang. A bootstrapping soft shrinkage approach for variable selection in chemical modeling. Analytica chimica acta. 2016, 908: 63-74. [PDF] 
  8. Liang Shen, Dongsheng Cao, Qingsong Xu, Xin Huang, Nan Xiao, Yizeng Liang. A novel local manifold-ranking based K-NN for modeling the regression between bioactivity and molecular descriptors. Chemometrics and Intelligent Laboratory Systems. 2016, 151: 71-77. [PDF] 
  9. Jian-Hua Huang, Hua-Lin Xie, Jun Yan, Dong-Sheng Cao, Hong-Mei Lu, Qing-Song Xu, Yi-Zeng Liang. Interpretation of type 2 diabetes mellitus relevant GC-MS metabolomics fingerprints by using random forests (vol 5, pg 4883, 2013). ANALYTICAL METHODS. 2013, 5(18): 4883-4889. [PDF] 
  10. Ming Wen, Bai-Chuan Deng, Dong-Sheng Cao, Yong-Huan Yun, Rui-Han Yang, Hong-Mei Lu, Yi-Zeng Liang. The model adaptive space shrinkage (MASS) approach: a new method for simultaneous variable selection and outlier detection based on model population analysis. Analyst. 2016, 141(19): 5586-5597. [PDF]

2015

  1. Jie Dong, Dong-Sheng Cao, Hong-Yu Miao, Shao Liu, Bai-Chuan Deng, Yong-Huan Yun, Ning-Ning Wang, Ai-Ping Lu, Wen-Bin Zeng, Alex Chen. ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. Journal of Cheminformatics 2015, 7:60 [PDF]
  2. Cao, D‐S., N. Xiao, Y‐J. Li, W‐B. Zeng, Y‐Z. Liang, A‐P. Lu, Q‐S. Xu, and A. F. Chen. "Integrating Multiple Evidence Sources to Predict Adverse Drug Reactions Based on a Systems Pharmacology Model." CPT: pharmacometrics & systems pharmacology 4, no. 9 (2015): 498-506. [PDF]
  3.  Cao, Dong-Sheng, Jie Dong, Ning-Ning Wang, Ming Wen, Bai-Chuan Deng, Wen-Bin Zeng, Qing-Song Xu, Yi-Zeng Liang, Ai-Ping Lu, and Alex F. Chen. "In silico toxicity prediction of chemicals from EPA toxicity database by kernel fusion-based support vector machines." Chemometrics and Intelligent Laboratory Systems 146 (2015): 494-502. [PDF]
  4. Huang, K., Liu, M., Liu, Z., Cao, D., Hou, J., & Zeng, W. (2015). Ratiometric and colorimetric detection of hydrogen sulfide with high selectivity and sensitivity using a novel FRET-based fluorescence probe. Dyes and Pigments, 118, 88-94.[PDF]
  5. Wang, J. B., Cao, D. S., Zhu, M. F., Yun, Y. H., Xiao, N., & Liang, Y. Z. (2015). In silico evaluation of logD7. 4 and comparison with other prediction methods. Journal of Chemometrics.[HTML]
  6. Xu, P., Huang, K., Cao, D., & Zeng, W. (2015). A Green and Efficient One-pot Synthesis of 1, 2, 3-Trisubstituted Pyrroles via Iodine-catalyzed Tandem Reaction. Letters in Organic Chemistry, 12(4), 290-298.[HTML]
  7. Deng, B. C., Yun, Y. H., Liang, Y. Z., Cao, D. S., Xu, Q. S., Yi, L. Z., & Huang, X. (2015). A new strategy to prevent over-fitting in partial least squares models based on model population analysis. Analytica Chimica Acta.[PDF]
  8. Huang, K., Liu, M., Wang, X., Cao, D., Gao, F., Zhou, K., ... & Zeng, W. (2015). Cascade reaction and FRET-based fluorescent probe for the colorimetric and ratiometric signaling of hydrogen sulfide. Tetrahedron Letters.[HTML]
  9. Cao, D. S., Liu, S., Zeng, W. B., & Liang, Y. Z. (2015). Sparse canonical correlation analysis applied to‐omics studies for integrative analysis and biomarker discovery. Journal of Chemometrics.[PDF]
  10. Cao, D., He, R., Zhang, M., Sun, Z., & Tan, T. (2015, March). Real-world gender recognition using multi-order LBP and localized multi-boost learning. In Identity, Security and Behavior Analysis (ISBA), 2015 IEEE International Conference on (pp. 1-6). IEEE.[HTML]
  11. Lv, Y., Cao, D., Guo, F., Qian, Y., Wang, C., & Wang, D. (2015). Abdominal wall reconstruction using a combination of free tensor fasciae lata and anterolateral thigh myocutaneous flap: a prospective study in 16 patients.The American Journal of Surgery.[HTML]
  12. Xiao, N., Cao, D. S., Zhu, M. F., & Xu, Q. S. (2015). protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences. Bioinformatics, btv042.[HTML]
  13. Wang, Y., Huang, J. J., Zhou, N., Cao, D. S., Dong, J., & Li, H. X. (2015). Incorporating PLS model information into particle swarm optimization for descriptor selection in QSAR/QSPR. Journal of Chemometrics.[PDF]
  14.  沈亮, 许青松, 曹东升, & 黄新. (2015). 基于 Markov 性的半监督流行学习算法研究. 中国科学: 数学, 5, 023.[PDF]

2014

  • [1] Xiao, Nan, Qing-Song Xu, and Dong-Sheng Cao. "protr: R package for generating various numerical representation schemes of protein sequence." (2014). [PDF]
  • [2] Xiao, Nan, Dong-Sheng Cao, and Qing-Song Xu. "enpls: R Package for Ensemble Partial Least Squares Regression." (2014). [PDF]
  • [3] Cao, D. S., Zhang, L. X., Tan, G. S., Xiang, Z., Zeng, W. B., Xu, Q. S., & Chen, A. F. (2014). Computational Prediction of Drug-Target Interactions Using Chemical, Biological, and Network Features. Molecular Informatics,33(10), 669-681.[PDF]
  • [4] Cao, D. S., Xiao, N., Xu, Q. S., & Chen, A. F. (2014). Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds, and their interactions. Bioinformatics, btu624.[PDF]
  • [5] Xiao, N., Cao, D., & Xu, Q. (2014). Rcpi: R/Bioconductor Package as an Integrated Informatics Platform in Drug Discovery.[PDF]
  • [6] Xiao, N., Xu, Q. S., & Cao, D. S. (2014). protr: Protein Sequence-Derived Structural and Physiochemical Descriptors Calculation with R.[PDF]
  • [7] Lou, Y., Peng, W. J., Cao, Y., Cao, D. S., Xie, J., & Li, H. H. (2014). The effectiveness of propranolol in treating infantile haemangiomas: a meta‐analysis including 35 studies. British journal of clinical pharmacology, 78(1), 44-57.[HTML]
  • [8] Shi, S. H., Cai, Y. P., Cai, X. J., Zheng, X. Y., Cao, D. S., Ye, F. Q., & Xiang, Z. (2014). A network pharmacology approach to understanding the mechanisms of action of traditional medicine: bushenhuoxue formula for treatment of chronic kidney disease. PloS one, 9(3), e89123.[PDF]
  • [9] Cao, D. S., Liu, S., Fan, L., & Liang, Y. Z. (2014). QSAR analysis of the effects of OATP1B1 transporter by structurally diverse natural products using a particle swarm optimization-combined multiple linear regression approach.Chemometrics and Intelligent Laboratory Systems, 130, 84-90.[PDF]
  • [10] Yun, Y. H., Cao, D. S., Tan, M. L., Yan, J., Ren, D. B., Xu, Q. S., ... & Liang, Y. Z. (2014). A simple idea on applying large regression coefficient to improve the genetic algorithm-PLS for variable selection in multivariate calibration. Chemometrics and Intelligent Laboratory Systems, 130, 76-83.[PDF]
  • [11] Yun, Y. H., Wang, W. T., Tan, M. L., Liang, Y. Z., Li, H. D., Cao, D. S., ... & Xu, Q. S. (2014). A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration. Analytica chimica acta, 807, 36-43.[PDF]
  • [12] Ying, X., Bing, Z., Bo-Tao, Z., Si-Yan, D., Li, Y., & Rou-Ke, L. (2014). Projected flood risks in China based on CMIP5. Advances in Climate Change Research, 5(2), 57-65.[HTML]
  • [13] Huang, X., Xu, Q. S., Cao, D. S., Luo, Y. P., & Liang, Y. Z. (2014). Kernel k-nearest neighbor classifier based on decision tree ensemble for SAR modeling analysis. Analytical Methods, 6(17), 6621-6627.[HTML]
  • [14] 贺敏, 曹东升, 梁逸曾, & 许青松. (2014). 预测药物和蛋白质相互作用划分天然药物化学成份升压机制的研究. 2014 年全国中药学术研讨会暨中国中西医结合学会第六届中药专业委员会换届改选会论文集.[HTML]
  • [15] Xiao, N., Xu, Q., & Cao, D. (2014). protr: Protein Sequence Descriptor Calculation and Similarity Computation with R. R package version 0.2-1, URL http://CRAN. R-project. org/package= protr.[PDF]

2013

  • [1] He, M., Cao, D. S., Liang, Y. Z., Li, Y. P., Liu, P. L., Xu, Q. S., & Huang, R. B. (2013). Pressor mechanism evaluation for phytochemical compounds using in silico compound–protein interaction prediction. Regulatory Toxicology and Pharmacology, 67(1), 115-124.[PDF]
  • [2] Cao, D. S., Liang, Y. Z., Yan, J., Tan, G. S., Xu, Q. S., & Liu, S. (2013). PyDPI: freely available Python package for chemoinformatics, bioinformatics, and chemogenomics studies. Journal of chemical information and modeling,53(11), 3086-3096.[PDF]
  • [3] Cao, D. S., Zhou, G. H., Liu, S., Zhang, L. X., Xu, Q. S., He, M., & Liang, Y. Z. (2013). Large-scale prediction of human kinase–inhibitor interactions using protein sequences and molecular topological structures. Analytica chimica acta, 792, 10-18.[PDF]
  • [4] Yun, Y. H., Li, H. D., Wood, L. R., Fan, W., Wang, J. J., Cao, D. S., ... & Liang, Y. Z. (2013). An efficient method of wavelength interval selection based on random frog for multivariate spectral calibration. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 111, 31-36.[HTML]
  • [5] Zhang, L., Tang, C., Cao, D., Zeng, Y., Tan, B., Zeng, M., ... & Liang, Y. (2013). Strategies for structure elucidation of small molecules using gas chromatography-mass spectrometric data. TrAC Trends in Analytical Chemistry, 47, 37-46.[PDF]
  • [6] Huang, J. H., He, R. H., Yi, L. Z., Xie, H. L., Cao, D. S., & Liang, Y. Z. (2013). Exploring the relationship between 5′ AMP-activated protein kinase and markers related to type 2 diabetes mellitus. Talanta, 110, 1-7.[PDF]
  • [7] Cao, D. S., Liang, Y. Z., Deng, Z., Hu, Q. N., He, M., Xu, Q. S., ... & Liu, S. (2013). Genome-scale screening of drug-target associations relevant to Ki using a chemogenomics approach. PloS one, 8(4), e57680.[PDF]
  • [8] Cao, D. S., Xu, Q. S., Hu, Q. N., & Liang, Y. Z. (2013). ChemoPy: freely available python package for computational biology and chemoinformatics.Bioinformatics, btt105.[PDF]
  • [9] Huang, X., Cao, D. S., Xu, Q. S., & Liang, Y. Z. (2013). A novel tree kernel partial least squares for modeling the structure–activity relationship. Journal of Chemometrics, 27(3-4), 43-49.[PDF]
  • [10] Cao, D. S., Xu, Q. S., & Liang, Y. Z. (2013). propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics, 29(7), 960-962.[PDF]
  • [11] He, M., Yan, J., Cao, D., Liu, S., Zhao, C., Liang, Y., ... & Zhang, Z. (2013). Identification of terpenoids from Ephedra combining with accurate mass and in-silico retention indices. Talanta, 103, 116-122.[PDF]
  • [12] Huang, X., Cao, D. S., Xu, Q. S., Shen, L., Huang, J. H., & Liang, Y. Z. (2013). A novel tree kernel support vector machine classifier for modeling the relationship between bioactivity and molecular descriptors. Chemometrics and Intelligent Laboratory Systems, 120, 71-76.[HTML]
  • [13] LUO, J., YU, F., LI, S. F., TANG, X. D., HU, Q., & CAO, D. (2013). Analysis of the Effects of 2011 Edition of Electronic Medical Record System and PASS Integration on Rational Drug Use Management in Our Hospital. China Pharmacy, 17, 005.[HTML]
  • [14] HE, R. H., LU, H. B., LI, Y. C., HUANG, J. G., YAN, J., CAO, D. S., ... & LIANG, Y. Z. (2013). Study on interpretation and reversion of tobaceo flavor based on bi-directional gradual simulation from chemical composition and fragrance characteristics. Chinese Journal of Analysis Laboratory, 3, 016.[HTML]
  • [15] YANG, R., LU, H. B., LI, Y. C., HUANG, J. G., YAN, J., CAO, D. S., ... & LIANG, Y. Z. (2013). Exploration of Tobacco Flavor Analysis and Formulation Based on GC-MS and Chemometrics. Journal of Instrumental Analysis, 3, 009.[HTML]
  • [16] Yun, Y. H., Li, H. D., Wood, L. R., Fan, W., Wang, J. J., Cao, D. S., ... & Liang, Y. Z. (2013). Spectr ochimica Acta Part A: Molecul ar and Biomo lecular Spectrosco py. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 111, 31-36.[PDF]
  • [17] 杨蕊, 卢红兵, 李燕春, 黄建国, 严军, 曹东升, ... & 梁逸曾. (2013). 复杂香精配方的化学组成解析及香韵识别. 分析科学学报, 3, 011.[HTML]
  • [18] 何瑞华, 卢红兵, 李艳春, 黄建国, 严军, 曹东升, ... & 梁逸曾. (2013). 基于化学组成及香韵特征双向渐近模拟烟用香精解析及还原方法研究. 分析试验室, 3, 016.[HTML]
  • [19] 杨蕊, 卢红兵, 李燕春, 黄建国, 严军, 曹东升, ... & 梁逸曾. (2013). 基于 GC-MS 和计算机智能系统的烟草香精剖析及复配方法初探. 分析测试学报, 32(3), 314-319.[PDF]
  • [20] Yun, Y. H., Liang, Y. Z., Xie, G. X., Li, H. D., Cao, D. S., & Xu, Q. S. (2013). A perspective demonstration on the importance of variable selection in inverse calibration for complex analytical systems. Analyst, 138(21), 6412-6421.[PDF]
  • [21] Huang, J. H., Xie, H. L., Yan, J., Cao, D. S., Lu, H. M., Xu, Q. S., & Liang, Y. Z. (2013). Interpretation of type 2 diabetes mellitus relevant GC-MS metabolomics fingerprints by using random forests. Analytical Methods,5(18), 4883-4889.[HTML]