LIST OF PUBLICATIONS

Publications in the Area of Computational Biology and Drug Design
  1. AggMapNet: enhanced and explainable low-sample omics deep learning with feature-aggregated multi-channel networks.Shen, W.X., Liu, Y., Chen, Y., Zeng, X., Tan, Y., Jiang, Y.Y. and Chen, Y.Z., Nucleic Acids Research 50(8), pp.e45-e45.(2022).
  2. Out-of-the-box deep learning prediction of pharmaceutical properties by broadly learned knowledge-based molecular representations. Shen, W.X., Zeng, X., Zhu, F., Qin, C., Tan, Y., Jiang, Y.Y. and Chen, Y.Z. Nat. Mach. Intell DOI: 10.1038/s42256-021-00301-6. 3, 334-343 (2021).
  3. MASI: microbiota-active substance interactions database. Zeng X, Yang X, Fan J, Tan Y, Ju L, Shen WX, Wang Y, Wang X, Chen W, Ju D and Chen YZ Nucleic Acids Res. 49(D1):D776-D782 (2020).
  4. Combining kinase inhibitors for optimally co-targeting cancer and drug escape by exploitation of drug target promiscuities. SY Chen, S Y Yang, X Zeng, F Zhu, Y Tan, Y Y Jiang and Y Z Chen Drug Dev. Res. DOI: 10.1002/ddr.21738. 82: 133-142 (2020).
  5. The pros and cons of traditional Chinese medicines in the treatment of COVID-19. Yali Wang, Xian Zeng, Yu fen Zhao, Wei ping, Chen Yu ZongChen Pharmacol. Res. DOI: 10.1016/j.phrs.2020.104873. 157: 104873 (2020).
  6. East meets West in COVID-19 therapeutics. SS Wang, X Zeng, Y L Wang, Y F Zhao, W P Chen, YZ Chen Pharmacol. Res. DOI: 10.1016/j.phrs.2020.105008. 159: 105008 (2020).
  7. Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs. Li YY, Li XX, Hong JJ, Wang YX, Fu JB, Yang H, Yu CY, Li FC, Hu J, Xue WW, Jiang YY, Chen YZ, Zhu F. Briefings in Bioinformatics. bby130. (2019).
  8. CMAUP: a database of collective molecular activities of useful plants. Zeng X, Zhang P, Wang Y, Qin C, Chen S, He W, Tao L, Tan Y, Gao D, Wang B, Chen Z, Chen W, Jiang YY, Chen YZ. Nucleic Acids Res. 47(D1): D1118-D1127. (2019).
  9. Drug sales confirm clinical advantage of multi-target inhibition of drug escapes by anticancer kinase inhibitors. Chen S, Yang SY, Chen Z, Tan Y, Jiang YY, Chen YZ. Drug Dev Res. doi: 10.1002/ddr.21486. [Epub ahead of print] (2018).
  10. Development of Ligand-based Big Data Deep Neural Network Models for Virtual Screening of Large Compound Libraries. Xiao T, Qi X, Chen Y, Jiang Y. Mol Inform. 37(11):e1800031. (2018).
  11. A benchmarking study on virtual ligand screening against homology models of human GPCRs. Lim VJY, Du W, Chen YZ, Fan H. Proteins. 86(9):978-989. (2018).
  12. Discovery of the Consistently Well-Performed Analysis Chain for SWATH-MS Based Pharmacoproteomic Quantification. Fu J, Tang J, Wang Y, Cui X, Yang Q, Hong J, Li X, Li S, Chen Y, Xue W, Zhu F. Front Pharmacol. 26;9:681. (2018).
  13. Antiproliferative activities of the second-generation antipsychotic drug sertindole against breast cancers with a potential application for treatment of breast-to-brain metastases. Zhang W, Zhang C, Liu F, Mao Y, Xu W, Fan T, Sun Q, He S, Chen Y, Guo W, Tan Y, Jiang Y. Sci Rep. 25;8(1):15753. (2018).
  14. Computational characterization of the selective inhibition of human norepinephrine and serotonin transporters by an escitalopram scaffold. Zheng G, Yang F, Fu T, Tu G, Chen Y, Yao X, Xue W, Zhu F. Phys Chem Chem Phys. 20(46):29513-29527. (2018).
  15. Phthalimide conjugations for the degradation of oncogenic PI3K. Li W, Gao C, Zhao L, Yuan Z, Chen YZ, Jiang YY. Eur J Med Chem. 151:237-247. (2018).
  16. What Contributes to Serotonin-Norepinephrine Reuptake Inhibitors' Dual-Targeting Mechanism? The Key Role of Transmembrane Domain 6 in Human Serotonin and Norepinephrine Transporters Revealed by Molecular Dynamics Simulation. Xue W, Yang F, Wang P, Zheng G, Chen YZ, Yao X, Zhu F. ACS Chem Neurosci. 9(5):1128-1140. (2018).
  17. Clinical Success of Drug Targets Prospectively Predicted by In Silico Study. Zhu F, Li XX, Yang SY, Chen YZ. Trends Pharmacol Sci. 39(3): 229-231. (2018).
  18. Exploring the Binding Mechanism of Metabotropic Glutamate Receptor 5 Negative Allosteric Modulators in Clinical Trials by Molecular Dynamics Simulations. TT. Fu, GX. Zheng, G. Tu, FY. Yang, Y.Z. Chen, XJ. Yao, XF. Li, WW. Xue, F. Zhu. ACS Chem. Neurosci. 9(6): 1492-1502. (2018).
  19. Computational identification of the binding mechanism of a triple reuptake inhibitor amitifadine for the treatment of major depressive disorder. WW. Xue, PP. Wang, G. Tu, FY. Yang, GX. Zheng, XF. Li, XX. Li, Y.Z. Chen, XJ. Yao, F. Zhu. Phys. Chem. Chem. Phys. 20: 6606-6616. (2018).
  20. NPASS: natural product activity and species source database for natural product research, discovery and tool development. X. Zeng, P. Zhang, W.D. He, C. Qin, S.Y. Chen, L. Tao, Y. Tan, D. Gao, B.H. Wang, Z. Chen, W.P. Chen, Y.Y. Jiang, Y.Z. Chen. Nucleic Acids Res. 46(D1):D1217-D1222. (2018).
  21. What Contributes to SNRIs' Dual-targeting Mechanism? The Key Role of TM6 Domain in hSERT and hNET Revealed by Molecular Dynamics Simulation. W Xue, F Yang, P Wang, G Zheng, Y Chen, X Yao, F Zhu. ACS Chem. Neurosci. (2018).
  22. Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate. C. Yu, X. Li, H. Yang, Y. Li, W. Xue, Y.Z. Chen, L. Tao, F. Zhu, Int. J. Mol. Sci. 19(1), 183; (2018).
  23. Discovery of indolylpiperazinylpyrimidines with dual-target profiles at adenosine A2A and dopamine D2 receptors for Parkinson's disease treatment. Shao YM, Ma X, Paira P, Tan A, Herr DR, Lim KL, Ng CH, Venkatesan G, Klotz KN, Federico S, Spalluto G, Cheong SL, Chen YZ, Pastorin G. PLoS One. 13(1):e0188212; (2018).
  24. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Y. H. Li, C. Y. Yu, X. X. Li, P. Zhang, J. Tang, Q. Yang, T. Fu, X. Zhang, X. Cui, G. Tu, Y. Zhang, S. Li, F. Yang, Q. Sun, C. Qin, X. Zeng, Z. Chen, Y. Z. Chen, and F. Zhu. Nucleic Acids Res. 46(D1):D1121-D1127. (2018).
  25. Predicting the Enzymatic Hydrolysis Half-lives of New Chemicals Using Support Vector Regression Models Based on Stepwise Feature Elimination. Shen W, Xiao T, Chen S, Liu F, Chen YZ, Jiang Y. Mol Inform. 36(11); DOI: 10.1002/minf.201600153; (2017).
  26. The database and bioinformatics studies of probiotics. L. Tao, B. Wang, Y. Zhong, S. H. Pow, X. Zeng, C. Qin, P. Zhang, S. Chen, W. He, Y. Tan, H. Liu, Y. Jiang, W. Chen, Y. Z. Chen. J. Agric. Food Chem. 65(35):7599-7606. (2017).
  27. Novel multi-substituted benzyl acridone derivatives as survivin inhibitors for hepatocellular carcinoma treatment. B. Zhang, N. Wang, C.L. Zhang, C.M. Gao, W. Zhang, K. Chen, W.B. Wu, Y.Z. Chen, C.Y. Tan, F. Liu, Y.Y. Jiang. Eur J Med Chem. 129:337-348 (2017).
  28. Discovery of 1-(3-aryl-4-chloropheny1)-3-(p-aryl)urea derivatives against breast cancer by inhibiting PI3K/Akt/mTOR and Hedgehog signalings. Li W, Sun Q, Song L, Gao C, Liu F, Chen YZ, Jiang YY. Eur J Med Chem. 141:721-733. (2017).
  29. Design, synthesis and anticancer potential of NSC-319745 hydroxamic acid derivatives as DNMT and HDAC inhibitors. Z.G. Yuan, Q.S. Sun, D. Lia, S.S. Miao, S.P. Chen, L. Song, C.M. Gao, Y.Z. Chen, C.Y. Tan, Y.Y. Jiang Eur J Med Chem. 134:281-292 (2017).
  30. Differentiating Physicochemical Properties between Addictive and Nonaddictive ADHD Drugs Revealed by Molecular Dynamics Simulation Studies. P.P. Wang, X.Y. Zhang, T.T. Fu, S. Li, B. Li, W.W. Xue, X.J. Yao, Y.Z. Chen, F. Zhu. ACS Chem Neurosci. 8(6):1416-1428 (2017).
  31. NOREVA: normalization and evaluation of MS-based metabolomics data. B. Li, J. Tang, Q.X. Yang, S. Li, X.J. Cui, Y.H. Li, Y.Z. Chen, W.W. Xue, X.F. Li, F. Zhu. Nucleic Acids Res. 45(W1):W162-W170. (2017).
  32. Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway. L. Huang, Y. Y. Jiang, Y. Z. Chen. Sci Rep. 7:40752 (2017).
  33. HEROD: a human ethnic and regional specific omics database. X. Zeng, L. Tao, P. Zhang, C. Qin, S. Chen, W. He, Y. Tan, H. X. Liu, S. Y. Yang, Z. Chen, Y. Y. Jiang, Y. Z. Chen. Bioinformatics. 33(20):3276-3282. (2017).
  34. A protein network descriptor server and its use in studying protein, disease, metabolic and drug targeted networks. P. Zhang, L. Tao, X. Zeng, C. Qin, S.Y. Chen, F. Zhu, Z.R. Li, Y.Y. Jiang, W.P. Chen, Y.Z. Chen. Brief Bioinform. 18(6):1057-1070 (2017).
  35. Discovery of novel dual VEGFR2 and Src inhibitors using a multistep virtual screening approach. S.Y. Chen, C. Qin, JE. Sin, X. Yang, L. Tao, X. Zeng, P. Zhang, CM. Gao, Y.Y. Jiang, C. Zhang, Y.Z. Chen, W.K. Chui. Future Med Chem. 9(1):7-24 (2017).
  36. Differentiating physicochemical properties between NDRIs and sNRIs clinically important for the treatment of ADHD.P. Wang, T. Fu, X. Zhang, F. Yang, G. Zheng, W. Xue, Y.Z. Chen, X. Yao, F. Zhu. Biochim Biophys Acta. 1861(11 Pt A):2766-2777 (2017).
  37. Design, synthesis and evaluation of azaacridine derivatives as dual-target EGFR and Src kinase inhibitors for antitumor treatment.Z. Cui, S. Chen, Y. Wang, C. Gao, Y.Z. Chen, C. Tan, Y.Y. Jiang. Eur J Med Chem. 18;136:372-381 (2017).
  38. Predicting the Enzymatic Hydrolysis Half-lives of New Chemicals Using Support Vector Regression Models Based on Stepwise Feature Elimination.W. Shen, T. Xiao, S. Chen, F. Liu, Y.Z. Chen, Y.Y. Jiang. Mol Inform. 36(11): UNSP 1600153. (2017).
  39. Olaparib hydroxamic acid derivatives as dual PARP and HDAC inhibitors for cancer therapy.Z. Yuan, S. Chen, Q. Sun, N. Wang, D. Li, S. Miao, C. Gao, Y.Z. Chen, C. Tan, Y.Y. Jiang. Bioorg Med Chem. 25(15):4100-4109. (2017).
  40. A dual-response quinoline-based fluorescent sensor for the detection of Copper (II) and Iron (III) ions in aqueous medium.B. Zhang, H. Liu, F. Wu, G. Hao, Y.Z. Chen, C. Tan, Y. Tan, Y.Y. Jiang. Sensors and Actuators B: Chemical. 243:765-774 (2017).
  41. Revealing Vilazodone's Binding Mechanism Underlying Its Partial Agonism to 5-HT1A Receptor in the Treatment of Major Depressive Disorder.G. Zheng, W. Xue, F. Yang, Y. Zhang, Y.Z. Chen, X. Yao, F. Zhu. Phys. Chem. Chem. Phys. 1861(11 Pt A):2766-2777 (2017).
  42. Synthesis and investigation of novel 6-(1,2,3-triazol-4-yl)-4-aminoquinazolin derivatives possessing hydroxamic acid moiety for cancer therapy.C. Ding, S. Chen, C. Zhang, G. Hu, W. Zhang, L. Li, Y.Z. Chen, C. Tan, Y.Y. Jiang. Bioorg Med Chem. 25(1):27-37 (2017).
  43. Design, synthesis and evaluation of acridine derivatives as multi-target Src and MEK kinase inhibitors for anti-tumor treatment.Z. Cui, X. Li, L. Li, B. Zhang, C. Gao, Y.Z. Chen, C. Tan, H. Liu, W. Xie, T. Yang, Y.Y. Jiang. Bioorg Med Chem. 24(2):261-9. (2016).
  44. A simple quinoline-derived fluorescent sensor for the selective and sequential detection of copper (II) and sulfide ions and its application in living-cell imaging.H. Liu, F. Wu, B. Zhang, C. Tan, Y.Z. Chen, G. Hao, Y. Tan, Y.Y. Jiang. RSC Advances. 6:77508-77514 (2016).
  45. PROFEAT Update: A Protein Features Web Server with Added Facility to Compute Network Descriptors for Studying Omics-Derived Networks. P. Zhang, L. Tao, X. Zeng, C. Qin, S.Y. Chen, F. Zhu, S.Y. Yang, Z.R. Li, W.P. Chen, Y.Z. Chen. J Mol Biol. pii:S0022-2836(16)30428-4. (2016).
  46. SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity. Y.H. Li, J.Y. Xu, L. Tao, X.F. Li, S. Li, X. Zeng, S.Y. Chen, P. Zhang, C. Qin, C. Zhang, Z. Chen, F. Zhu, Y.Z. Chen. PLoS One.11(8):e0155290 (2016).
  47. Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach. S.Y. Chen, P. Zhang, X. Liu, Q. Chu, L. Tao, C. Zhang, S.Y. Yang, Y.Z. Chen, W.K. Chui. J. Mol. Graph. Model. 67:102-110 (2016).
  48. Identification of the inhibitory mechanism of FDA approved selective serotonin reuptake inhibitors: an insight from molecular dynamics simulation study. W. Xue, P. Wang, B. Li, Y. Li, X. Xu, F. Yang, X. Yao, Y.Z. Chen, F. Xu, F. Zhu. Phys Chem Chem Phys. 18(4):3260-71 (2016).
  49. Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information. H. Yang, C. Qin, Y.H. Li, L. Tao, J. Zhou, C.Y. Yu, F. Xu, Z. Chen, F. Zhu, Y.Z. Chen. Nucleic Acids Res. 44(D1):D1069-74 (2016).
  50. The Assessment of the Readiness of Molecular Biomarker-Based Mobile Health Technologies for Healthcare Applications. C. Qin, L. Tao, Y.H. Pang, C. Zhang, S.Y. Chen, P. Zhang, Y. Tan, Y.Y. Jiang, Y.Z. Chen. Sci Rep. 5:17854 (2015).
  51. Co-targeting cancer drug escape pathways confers clinical advantage for multi-target anticancer drugs. L. Tao, F. Zhu, F. Xu, Z. Chen, Y.Y. Jiang, Y.Z. Chen. Pharmacol Res. 102:123-131 (2015).
  52. Fluorescence array-based sensing of metal ions using conjugated polyelectrolytes. Y. Wu, Y. Tan, J. Wu, S. Chen, Y.Z. Chen, X. Zhou, Y. Jiang and C. Tan. ACS Appl Mater Interfaces. 7(12):6882-8 (2015).
  53. Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools. L. Tao, P. Zhang, C. Qin, S.Y. Chen, C. Zhang, Z. Chen, F. Zhu, S.Y. Yang, Y.Q. Wei, Y.Z. Chen. Adv Drug Deliv Rev. 86:83-100 (2015).
  54. Physicochemical profiles of the marketed agrochemicals and clues for agrochemical lead discovery and screening library development. H.B. Rao, C.X. Huangfu, Y.Y. Wang, X.X. Wang, T.S. Tang, X.Y. Zeng, Z.R. Li and Y.Z. Chen. Mol Inform. 34(5):331-338 (2015).
  55. A preclinical evaluation of SKLB261, a multikinase inhibitor of EGFR/Src/VEGFR2, as a therapeutic agent against pancreatic cancer. Y. Pan, M. Zheng, L. Zhong, J. Yang, S. Zhou, Y. Qin, R. Xiang, Y.Z. Chen and S.Y. Yang. Mol Cancer Ther. 14(2):407-18 (2015).
  56. Clustered Distribution of Natural Product Leads of Drugs in the Chemical Space as Influenced by the Privileged Target-Sites. L. Tao, F. Zhu, C. Qin, C. Zhang, S.Y. Chen, P. Zhang, C.L. Zhang, C.Y. Tan, C.M. Gao, Z. Chen, Y.Y. Jiang and Y.Z. Chen. Sci Rep. 5:9325 (2015).
  57. CFam: A Chemical Families Database Based on Iterative Selection of Functional Seeds and Seed-Directed Compound Clustering. C. Zhang, L. Tao, C. Qin, P. Zhang, S.Y. Chen, X. Zeng, F. Xu, Z. Chen, S.Y. Yang and Y.Z. Chen. Nucleic Acids Res. 43:D558-65 (2015).
  58. Nature's contribution to today's pharmacopeia. L. Tao, F. Zhu, C. Qin, C. Zhang, F. Xu, C.Y. Tan, Y.Y. Jiang, Y.Z. Chen. Nat Biotechnol. 32(10):979-80 (2014).
  59. Optimization of culture conditions of Bacillus subtilis natto and preparation of freeze-dried powders as a potentially novel antithrombotic probiotic. M.F. He, K.J. Tang, B.H. Wang, M.H. Qu, L.P. Lin, Y.Z. Chen and W.P. Chen. J. Pure Appl Microbiology. 8(2):1619-1625 (2014).
  60. A Resource for Facilitating the Development of Tools in the Education and Implementation of Genomics-Informed Personalized Medicine. C. Zhang, C. Qin, L. Tao, F. Zhu, S.Y. Chen, P. Zhang, S.Y. Yang, Y. Q. Wei, Y.Z. Chen. Clin Pharmacol Ther. 95:590-591(2014).
  61. Therapeutic target database update 2014: a resource for targeted therapeutics. C. Qin, C. Zhang, F. Zhu, F. Xu, S.Y. Chen, P. Zhang, Y.H. Li, S.Y. Yang, Y.Q. Wei, L. Tao and Y.Z. Chen. Nucleic Acids Res. 42(1):D1118-23 (2014).
  62. Multitarget inhibitors derived from crosstalk mechanism involving VEGFR2.C. Ding, C. Zhang, M. Zhang, Y.Z. Chen, C. Tan, Y. Tan, Y.Y Jiang. Future Med Chem.. 6(16):1771-89 (2014).
  63. A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.S. Zhou, G. Li, L.Y. Huang, H.Z. Xie, Y.L. Zhao, Y.Z. Chen, L.L. Li, S.Y. Yang. Computers in Biology and Medicine. 51:122-127 (2014).
  64. Exploration of N-(2-aminoethyl)piperidine-4-carboxamide as a potential scaffold for development of VEGFR-2, ERK-2 and Abl-1 multikinase inhibitor. F. Jin, D. Gao, Q. Wu, F. Liu, Y.Z. Chen, C. Tan and Y. Jiang Bioorg Med Chem. 21(18):5694-706 (2013).
  65. Quantitative structure-activity relationship study of influenza virus neuraminidase A/PR/8/34 (H1N1) inhibitors by genetic algorithm feature selection and support vector regression. Y. Cong, B.K. Li, X.G. Yang, Y. Xue, Y.Z. Chen and Y. Zeng Chemometr. Intell. Lab. 127:35-42 (2013).
  66. In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method. B.K. Li, Y. Cong, X.G. Yang, Y. Xue and Y.Z. Chen. Comput Biol Med. 43(4):395-404 (2013).
  67. Exploration of 1-(3-chloro-4-(4-oxo-4H-chromen-2-yl)phenyl)-3-phenylurea derivatives as selective dual inhibitors of Raf1 and JNK1 kinases for anti-tumor treatment. F. Jin, D. Gao, C. Zhang, F. Liu, B. Chu, Y. Chen, Y.Z. Chen, C. Tan and Y. Jiang. Bioorg Med Chem. 21(3):824-831 (2013).
  68. Predicting Targeted Polypharmacology for Drug Repositioning and Multi-Target Drug Discovery. X. Liu, F. Zhu, X.H. Ma, Z. Shi, S.Y. Yang, Y.Q. Wei and Y.Z. Chen. Curr Med Chem. 20(13):1646-1661 (2013).
  69. Toxicogenomic analysis suggests chemical-induced sexual dimorphism in the expression of metabolic genes in zebrafish liver. X. Zhang, C.Y. Ung, S.H. Lam, J. Ma, Y.Z. Chen, L. Zhang, Z. Gong and B. Li. PLoS One. 7(12):e51971 (2012).
  70. Development and experimental test of support vector machines virtual screening method for searching Src inhibitors from large compound libraries. B.C. Han, X.H. Ma, R.Y. Zhao, J.X. Zhang, X.N. Wei, X.H. Liu, X. Liu, C.L. Zhang, C.Y. Tan, Y.Y. Jiang and Y.Z. Chen. Chem Cent J. 6:139 (2012).
  71. A global characterization and identification of multifunctional enzymes. X.Y. Cheng, W.J. Huang, S.C. Hu, H.L. Zhang, H. Wang, J.X. Zhang, H.H. Lin, Y.Z. Chen, Q. Zou and Z.L. Ji. PLoS ONE. 7(6):e38979 (2012).
  72. Prediction of human major histocompatibility complex class II binding peptides by continuous kernel discrimination method. J. He, G. Yang, H. Rao, Z. Li, X. Ding and Y.Z. Chen. Artif Intell Med. 55(2):107-15 (2012).
  73. Synthesis and Cytotoxic Activity of Some Novel N-Pyridinyl-2-(6-phenylimidazo[2,1-b]thiazol-3-yl)acetamide Derivatives. H. Ding, Z. Chen, C. Zhang, T. Xin, Y. Wang, H. Song, Y. Jiang, Y.Z. Chen, Y. Xu and C. Tan C. Molecules. 17(4):4703-16 (2012).
  74. Neighbor communities in drug combination networks characterize synergistic effect. J. Zou, P. Ji, Y.L. Zhao, L.L. Li, Y.Q. Wei, Y.Z. Chen and S.Y. Yang. Mol Biosyst. 8(12):3185-96 (2012).
  75. The interprotein scoring noises in glide docking scores. W. Wang, X. Zhou, W. He, Y. Fan, Y.Z. Chen and X. Chen. Poteins. 80(1):169-83 (2012).
  76. MicrobPad MD: Microbial Pathogen Diagnostic Methods Database. B.C. Han, X.N. Wei, J.X. Zhang, N.Q.T. Truong, C.L. Westgate, R.Y. Zhao and Y.Z. Chen. Infect. Genet. Evol. 13:261-6 (2012).
  77. What does it take to synergistically combine sub-potent natural products into drug-level potent combinations? C. Qin, K.L. Tan, C.L. Zhang, C.Y. Tan, Y.Z. Chen and Y.Y. Jiang. PLoS ONE. 7(11):e49969 (2012).
  78. Virtual screening methods as tools for drug lead discovery from large chemical libraries. X.H. Ma, F. Zhu, X. Liu, Z. Shi, J.X. Zhang, S.Y. Yang, Y.Q. Wei and Y.Z. Chen. Curr Med Chem.. 19(32):5562-71 (2012).
  79. In silico prediction of adverse drug reactions and toxicities based on structural, biological and clinical data. X. Liu, Z. She, Y. Xue, Z.R. Li, S.Y. Yang and Y.Z. Chen. Current Drug Safety. 7(3):225-37 (2012).
  80. Drug Discovery Prospect from Untapped Species: Indications from Approved Natural Product Drugs. F. Zhu, X.H. Ma, C. Qin, L. Tao, X. Liu, Z. Shi, C.L. Zhang, C.Y. Tan, Y.Y. Jiang and Y.Z. Chen. PLoS ONE. 7(7):e39782 (2012).
  81. Analysis of bypass signaling in EGFR pathway and profiling of bypass genes for predicting response to anticancer EGFR tyrosine kinase inhibitors. J.X. Zhang, J. Jia, F. Zhu, X.H. Ma, B.C. Han, X.N. Wei, C.Y. Tan, Y.Y. Jiang and Y.Z. Chen. Mol. BioSyst. Advance Article, 8(10):2645-56 (2012).
  82. A two-step Target Binding and Selectivity Support Vector Machines Approach for Virtual Screening of Dopamine Receptor Subtype-selective Ligands. J.X. Zhang, B.C. Han, X.N. Wei, C.Y. Tan, Y.Z. Chen and Y.Y. Jiang. PLoS ONE. 7(6):e39076 (2012).
  83. Identification of DNA adduct formation of small molecules by molecular descriptors and machine learning methods. H.B. Rao, X.Y. Zeng, Y.Y. Wang, H. He, F. Zhu, Z.R. Li and Y.Z. Chen. Mol. Simul. 38(4):259-273 (2012)
  84. Therapeutic Target Database Update 2012: A Resource for Facilitating Target-Oriented Drug Discovery. F. Zhu, Z. Shi, C. Qin, L. Tao, X. Liu, F. Xu, L. Zhang, Y. Song, X.H. Liu, J.X. Zhang, B.C. Han, P. Zhang and Y.Z. Chen. Nucleic Acids Res. 40(D1):D1128-D1136 (2012). Pubmed
  85. Combinatorial Support Vector Machines Approach for Virtual Screening of Selective Multi-Target Serotonin Reuptake Inhibitors from Large Compound Libraries. Z. Shi, X.H. Ma, C. Qin, J. Jia, Y.Y. Jiang, C.Y. Tan, Y.Z. Chen. J Mol Graph Model. 32:49-66 (2012).
  86. Dissipative particle dynamics simulation of field-dependent DNA mobility in nanoslits. Kun Yan, Yu-Zong Chen, Jongyoon Han, Gui-Rong Liu, Jian-Sheng Wang, Nicoloas G. Hadjiconstantinou. Microfluid Nanofluid. 12(1-4):157-163 (2012).
  87. Simulating EGFR-ERK Signaling Control by Scaffold Proteins KSR and MP1 Reveals Differential Ligand-Sensitivity Co-Regulated by Cbl-CIN85 and Endophilin. Huang L, Pan CQ, Li B, Tucker-Kellogg L, Tidor B, Yuzong Chen, Boon Chuan Low. PLoS ONE. 6(8):e22933 (2011).
  88. An Integrated Mathematical Model of Thrombin-, Histamine-and VEGF-Mediated Signalling in Endothelial Permeability. Wei XN, Han BC, Zhang JX, Liu XH, Tan CY, Jiang YY, Low BC, Tidor B, Chen YZ. BMC Syst Biol. Jul 15;5(1):112 (2011). Pubmed
  89. Discovery of benzimidazole derivatives as novel multi-target EGFR, VEGFR-2 and PDGFR kinase inhibitors. Li Y, Tan C, Gao C, Zhang C, Luan X, Chen X, Liu H, Chen Y and Jiang Y. Bioorg Med Chem. 19(15):4529-35 (2011). Pubmed
  90. Clustered patterns of species origins of nature-derived drugs and clues for future bioprospecting. F. Zhu, C. Qin, L. Tao, X. Liu, Z. Shi, X.H. Ma, J. Jia, Y. Tan, C. Cui, J.S. Lin, C.Y. Tan, Y.Y. Jiang and Y.Z. Chen. PNAS. 108(31):12943-8 (2011). Pubmed
  91. Exploration of acridine scaffold as a potentially interesting scaffold for discovering novel multi-target VEGFR-2 and Src kinase inhibitors. X. Luan, C. Gao, N. Zhang, Y.Z. Chen, Q. Sun, C.Y. Tan, H. Liu, Y. Jin and Y.Y. Jiang. Bioorg Med Chem. 19(11):3312-9 (2011). Pubmed
  92. The Therapeutic Target Database: an internet resource for the primary targets of approved, clinical trial and experimental drugs. X. Liu, F. Zhu, X.H. Ma, L, Tao, J.X. Zhang, S.Y. Yang, Y.C. Wei and Y.Z. Chen. Expert Opin Ther Targets. 15(8):903-12 (2011). Pubmed
  93. Effect of Training Data Size and Noise Level on Support Vector Machines Virtual Screening of Genotoxic Agents from Large Compound Libraries. Pankaj Kumar, X.H. Ma, X.H. Liu, J. Jia, B.C. Han, Y. Xue, Z.R. Li, S.Y. Yang, Y.C. Wei and Y.Z. Chen. J Comput Aided Mol Des. 25(5):455-67 (2011).
  94. Exploration of (S)-3-aminopyrrolidine as a potentially interesting scaffold for discovery of novel Abl and PI3K dual inhibitors. C.L. Zhang, C.Y. Tan, X.Y. Zu, X. Zhai, F. Liu, B.Z. Chu, X.H. Ma, Y.Z. Chen, P. Gong, Y.Y. Jiang. Eur J Med Chem. 46(4), 1404-1414 (2011). Pubmed
  95. Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. H.B. Rao, F. Zhu, G.B. Yang, Z.R. Li, and Y.Z. Chen. Nucleic Acids Res. 39(Database issue):W385-90(2011). Pubmed
  96. Dispersive Transport of Biomolecules in Periodic Energy Landscapes with application to Nanofilter Sieving Arrays. Li, Z. R., G. R. Liu, N. G. Hadjiconstantinou, J. Han, J. S. Wang, and Y. Z. Chen. Electrophoresis 32, 506-517 (2011). Pubmed
  97. Classification Models for Acetylcholinesterase Inhibitors Based on Machine Learning Methods. Yang GB, Li ZR , Rao HB, Li XY, Chen YZ. Acta Physico-Chimica Sinica Volume: 26 Issue: 12 Pages: 3351-3359 (2010)
  98. HIT: linking herbal active ingredients to targets. H. Ye, L. Ye, H. Kang, D.F. Zhang, L. Tao, K.L. Tang, X.P. Liu X, R.X. Zhu, Q. Liu, Y.Z. Chen, Y.X. Li and Z.W. Cao. Nucleic Acids Res. 39 (suppl_1): D1055-D1059 (2011). Pubmed
  99. Virtual screening of selective multi-target kinase inhibitors by combinatorial support vector machines. X.H. Ma, R. Wang, C.Y. Tan, Y.Y. Jiang, T. Lu, H.B. Rao, X.Y. Li, M.L. Go, B.C. Low and Y.Z. Chen.Mol. Pharmaceutics. 7(5):1545-60(2010). Pubmed
  100. An insight into the opening path to semi-open conformation of HIV-1 protease by molecular dynamics simulation. T. Lu, Y.Z. Chen. and X.Y. Li. AIDS. 24(8):1121-5(2010). Pubmed
  101. In Silico Prediction and Screening of gamma-Secretase Inhibitors by Molecular Descriptors and Machine Learning Methods. X.G. Yang, W. Lv, Y.Z. Chen. and Y. Xue.J Comput Chem. 31(6):1249-58(2010). Pubmed
  102. Identifying Novel Type ZBGs and Non-hydroxamate HDAC Inhibitors Through a SVM Based Virtual Screening Approach. X.H. Liu, H.Y. Song, J.X. Zhang, B.C. Han, X.N. Wei, X.H. Ma, W.K. Chui, Y.Z. Chen. Mol Inf. 29(5): 407-20(2010)
  103. In-Silico Approaches to Multi-Target Drug Discovery. H.X. Ma, Z. Shi, C.Y. Tan, Y.Y. Jiang, M.L. Go, B.C. Low and Y.Z. Chen.Pharm Res. 27(5):2101-10(2010). Pubmed
  104. Virtual Screening Prediction of New Potential Organocatalysts for Direct Aldol Reactions. X.H. Liu, H.Y. Song, H.X. Ma, M.J. Lear and Y.Z. Chen. J Mol Catal A: Chem. 319:114-118(2010).
  105. Update of TTD: Therapeutic Target Database. F. Zhu, B.C. Han, P. Kumar, X.H. Liu, X.H. Ma, X.N. Wei, L. Huang, Y.F. Guo, L.Y. Han, C.J. Zheng, Y.Z. Chen. Nucleic Acids Res. 38(Database issue):D787-91(2010). Pubmed
  106. Virtual Screening of Abl Inhibitors from Large Compound Libraries by Support Vector Machines. X.H. Liu, X.H. Ma, C.Y. Tan, Y.Y. Jiang, M.L. Go, B.C. Low and Y.Z. Chen. J Chem Info Model. 49(9):2101-10(2009). Pubmed
  107. Prediction of Antibacterial Compounds by Machine Learning Approaches. X.G. Yang, D. Chen, M. Wang, Y. Xue and Y.Z. Chen. J Comput Chem. 30(8):1202-11(2009). Pubmed
  108. Identification of Small Molecule Aggregators from Large Compound Libraries by Support Vector Machines. H.B. Rao, Z.R. Li, X.Y. Li, X.H. Ma, C.Y. Ung, H. Li, X.H. Liu and Y.Z. Chen. J Comput Chem. 2010 Mar;31(4):752-63.
  109. What are next generation innovative therapeutic targets? Clues from genetic, structural, physicochemical and system profile of successful targets. F. Zhu, L.Y. Han, C.J. Zheng, B. Xie, M.T. Tammi, S.Y. Yang, Y.Q. Wei and Y.Z. Chen. J Pharmacol Exp Ther. 330(1):304-15(2009).
  110. Synergistic therapeutic actions of herbal ingredients and their mechanisms from molecular interaction and network perspectives X. H. Ma, C.J. Zheng, L.Y. Han, B. Xie, J. Jia, Z.W. Cao, Y.X. Li and Y. Z. Chen. Drug Discov Today. 14:579-588(2009).
  111. Pathway sensitivity analysis for detecting pro-proliferation activities of oncogenes and tumor suppressors of EGFR-ERK pathway at altered protein levels H. Li, C. Y. Ung, X. H. Ma, X. H. Liu, B. W. Li, B. C. Low and Y. Z. Chen. Cancer. 15(18):4246-4263(2009).
  112. Genome-Scale Search of Tumor-Specific Antigens by Collective Analysis of Mutations, Expressions and T-Cell Recognition. J. Jia, Cui. J. , X. H. Liu, J. H. Han, S. Y. Yang, Y. Q. Wei, and Y. Z. Chen. Mol. Immunol. 46:1824-1829(2009).
  113. Simulation of Crosstalk between Small GTPase RhoA and EGFR-ERK Signaling Pathway via MEKK1. H. Li, C. Y. Ung, X. H. Ma, B. W. Li, B. C. Low, Z. W. Cao and Y. Z. Chen.Bioinformatics. 25(3):358-64(2009). Pubmed
  114. Update of KDBI: Kinetic Data of Bio-molecular Interaction Database. P. Kumar, Z.L. Ji, B.C. Han, Z. Shi, J. Jia, Y.P, Wang, Y.T. Zhang, L. Liang, and Y. Z. Chen. Nucleic Acids Res. 37(Database issue): D636-41(2009). Pubmed
  115. Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries. X.H. Ma, J. Jia, F. Zhu, Y. Xue, Z.R. Li and Y.Z. Chen. Comb. Chem. High Throughput Screen. 12(4):344-357(2009).
  116. Simulation of DNA Electrophoresis in Systems of Large Number of Solvent Particles by Coarse-Grained Hybrid Molecular Dynamics Approach. R. Wang, J.S. Wang, G.R. Liu, J.Y. Han, N.G. Hadjiconstantinou and Y. Z. Chen. J Comput Chem. 30(4):505-13(2009). Pubmed
  117. Mechanisms of drug combinations from interaction and network perspectives J. Jia, F. Zhu, X.H. Ma, Z.W. Cao, Y.X. Li and Y.Z. Chen. Nat. Rev. Drug Discov., 8(2):111-28(2009). Pubmed
  118. Trends in the Exploration of Anticancer Targets and Strategies in Enhancing the Efficacy of Drug Targeting. F. Zhu, C.J. Zheng, L.Y. Han, B. Xie, J. Jia, X. Liu, M.T. Tammi, S.Y. Yang, Y.Q. Wei and Y.Z. Chen. Curr Mol Pharmacol. 1(3):213-232(2008).
  119. Simulation of the Regulation of EGFR Endocytosis and EGFR-ERK Signaling by Endophilin-Mediated RhoA-EGFR Crosstalk. C.Y. Ung, H. Li, X.H. Ma, J. Jia, B.W. Li, B.C. Low and Y.Z. Chen.FEBS Lett. 582:2283-2290 (2008).Pubmed
  120. Evaluation of Virtual Screening Performance of Support Vector Machines Trained by Sparsely Distributed Active Compounds. X.H. Ma, R. Wang, S.Y. Yang, Z.R. Li, Y. Xue, Y.Q. Wei, B.C. Low and Y. Z. Chen.J Chem Inf Model. 48(6):1227-1237 (2008). Pubmed
  121. 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. L.Y. Han, X.H. Ma, H.H. Lin, J. Jia, F. Zhu, Y. Xue, Z.R. Li, Z.W. Cao, Z.L. Ji, Y.Z. Chen. J Mol Graph Mod. 26(8):1276-1286 (2008) Pubmed
  122. Prediction of Antibiotic Resistance Proteins from Sequence Derived Properties Irrespective of Sequence Similarity. H.L. Zhang, H.H. Lin, L. Tao, X.H. Ma, J.L. Dai, J.Jia, Z.W. Cao.Int J Antimicrob Agents. 32(3):221-226 (2008). Pubmed
  123. Advances in Machine Learning Prediction of Toxicological Properties and Adverse Drug Reactions of Pharmaceutical Agents. X.H. Ma, R. Wang, Y. Xue, Z.R. Li, S.Y. Yang, Y.Q. Wei and Y.Z. Chen.Curr Drug Saf. 3(2):100-114 (2008). Pubmed
  124. Homology-Free Prediction of Functional Class of Proteins and Peptides by Support Vector Machines. F. Zhu, L.Y. Han, X. Chen, H.H. Lin, S. Ong, B. Xie, H.L. Zhang, Y.Z. Chen. Curr. Protein Pept. Sci.. 9:70-95 (2008).
  125. Learning the drug target-likeness of a protein H. Xu, H.Y. Xu, M. Lin, W. Wang, Z.M. Li, J.J. Huang, Y.Z Chen, X. Chen.Proteomics. 7(23):4255-4263 (2007).Pubmed
  126. Efficacy of different protein descriptors in predicting protein functional families. S. Ong, H.H. Lin, Y.Z. Chen, Z.R. Li, Z.W. Cao. BMC Bioinforamtics. 8:300 (2007).Pubmed
  127. DITOP: drug-induced toxicity related protein database. J.X Zhang, W.J. Huang, J.H. Zeng, W.H. Huang, Y. Wang, R. Zhao, B.C. Han, Q.F. Liu, Y.Z. Chen, Z.L. Ji. Bioinforamtics. 23(13):1710-1712 (2007).Pubmed
  128. Formulation Development of Transdermal Dosage Forms: Quantitative Structure-Activity Relationship Model for Predicting Activities of Terpenes that Enhance Drug Penetration Through Human Skin . L. Kang, C.W. Yap, P.F. Lim, Y.Z. Chen,P.C. Ho, Y.W. Chan, G.P. Wang, S.Y. Chan. J Control Release. 120(3):211-219 (2007).Pubmed
  129. Trends in the Exploration of Therapeutic Targets for the Treatment of Endocrine, Metabolic and Immune Disorders. X. Chen, C.J. Zheng, L.Y. Han, B. Xie and Y.Z. Chen. Endocr Metab Immune Disord Drug Targets 7(3):225-231 (2007).Pubmed
  130. Prediction of Functional Class of Proteins and Peptides Irrespective of Sequence Homology by Support Vector Machines. Z.Q. Tang, H.H. Lin, H.L. Zhang, L.Y. Han, X. Chen, Y.Z. Chen. Bioinformatics Biol. Insights 1:19-47 (2007).
  131. Derivation of Stable Microarray Cancer-differentiating Signatures by a Feature-selection Method Incorporating Consensus Scoring of Multiple Random Sampling and Gene-Ranking Consistency Evaluation. Z.Q. Tang, L.Y. Han, H.H. Lin, J. Cui, J. Jia, B.C. Low, B.W. Li, Y.Z. Chen. Cancer Res. 67(20):9996-10003 (2007).Pubmed
  132. Regression Methods for Developing QSAR and QSPR Models to Predict Compounds of Specific Pharmacodynamic, Pharmacokinetic and Toxicological Properties. C. W. Yap, H. Li, Z. L. Ji and Y. Z. Chen. Mini. Rev. Med. Chem. 7(11):1097-1107 (2007). Pubmed
  133. Prediction of Factor Xa Inhibitors by Machine Learning Methods. H.H Lin, L.Y. Han, C.W. Yap, Y. Xue, X.H. Liu, F. Zhu, and Y.Z Chen. J. Mol. Graph. Mod. 26(2):505-518 (2007) Pubmed
  134. AAIR: Antibody Antigen Information Resource. Z.Q.Tang, L.Y. Han, B. Xie, C.Y. Ung, L. Jiang, Z.W. Cao, and Y.Z Chen. J Immunol 178(8): 4705 (2007).Pubmed
  135. Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness. L.Y. Han, C.J. Zheng, B. Xie, J. Jia, X.H. Ma, F. Zhu, H.H. Lin, X. Chen, and Y.Z. Chen. Drug Discov. Today 12(7-8): 304-313 ( 2007).Pubmed
  136. Advances in exploration of machine learning methods for predicting functional class and interaction profiles of proteins and peptides irrespective of sequence homology J. Cui, L.Y. Han, H.H. Lin, Z.Q. Tang, Z.L. Ji, Z.W. Cao, Y.X. Li, and Y.Z. Chen. Curr. Bioinformatics 2(2): 95-112 (2007).
  137. Machine Learning Approaches for Predicting Compounds That Interact with Therapeutic and ADMET Related Proteins. H. Li, C.W. Yap, C.Y. Ung, Y. Xue, Z.R. Li, L.Y. Han, H.H. Lin and Y.Z. Chen. J. Pharm. Sci. 96(11):2838-2860 (2007).Pubmed
  138. Are Herb-Pairs of Traditional Chinese Medicine Distinguishable from Others? Pattern Analysis and Artificial Intelligence Classification Study of Traditionally-Defined Herbal Properties. C.Y. Ung, H. Li, Z.W. Cao, Y.X. Li and Y.Z. Chen. J. Ethnopharmacol. 111(2):371-377 (2007).Pubmed
  139. MODEL -- Molecular Descriptor Lab: A Web-Based Server for Computing Structural and Physicochemical Features of Compounds. Z.R. Li, L. Y. Han, Y. Xue, C. W. Yap, H. Li, L. Jiang, and Y. Z. Chen. Biotechnol. Bioeng 97(2):389-96 (2007).Pubmed
  140. Computer Prediction of Cardiovascular and Hematological Agents by Statistical Learning Methods X. Chen, H. Li, C.W. Yap, C.Y. Ung, L. Jiang, Z.W. Cao, Y.X. Li and Y.Z. Chen Cardiovasc. Hematol. Agents Med. Chem. 5(1):11-19 ( 2007).Pubmed
  141. In Silico Prediction of Pregnane X Receptor Activators by Machine Learning Approaches. C.Y. Ung, H. Li, C.W. Yap and Y.Z. Chen. Mol. Pharmacol. 71(1):158-168 (2007).Pubmed
  142. PharmGED: Pharmacogenetic Effect Database B. Xie, C.J. Zheng, L. Y. Han, S. Ong, J. Cui, H.L. Zhang, L. Jiang, X. Chen and Y. Z. Chen. Clin. Pharmacol. Ther. 81(1): 29 (2007).Pubmed
  143. Prediction of MHC-Binding Peptides of Flexible Lengths from Sequence-Derived Structural and Physicochemical Properties J. Cui, L.Y. Han, H.H. Lin, H.L. Zhang, Z.Q. Tang, C.J. Zheng, Z.W. Cao, and Y.Z. Chen . Mol. Immunol. 44: 866-877 (2007).Pubmed
  144. Computer Prediction of Allergen Proteins from Sequence-Derived Protein Structural and Physicochemical Properties J. Cui, L.Y. Han, H.H. Lin, Z.Q. Tang, C.J. Zheng, Z.W. Cao, and Y.Z. Chen . Mol. Immunol. 44(4): 514-520 (2007).Pubmed
  145. PharmGED: Pharmacogenetic Effect Database. C.J.Zheng, L.Y.Han, B.Xie, C.Y.Liew, S.Ong, J.Cui, H.L.Zhang, Z.Q.Tang, S.H.Gan, L.Jiang and Y.Z. Chen. Nucleic Acids Res. 35:D794-D799 Sp. Iss. SI (2007)>.Pubmed
  146. Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation. X Chen, H Zhou, YB Liu, JF Wang, H Li, CY Ung, LY Han, ZW, Cao and YZ Chen Br. J. Pharmacol. 149(8):1092-1103 (2006).Pubmed
  147. Prediction of the Functional Class of Metal-Binding Proteins from Sequence Derived Physicochemical Properties by Support Vector Machine Approach. H.H. Lin, L.Y. Han, H.L. Zhang, C.J. Zheng, B. Xie, and Y.Z. Chen. BMC Bioinformatics 7(Suppl 5): S13 (2006).Pubmed
  148. Classification of a Diverse Set of Tetrahymena Pyriformis Toxicity Chemical Compounds from Molecular Descriptors by Statistical Learning Methods Y. Xue, H. Li, C.Y. Ung, C.W. Yap and Y.Z. Chen Chem. Res. Toxicol. 19 (8): 1030-1039 (2006). Pubmed
  149. Usefulness of Traditionally-Defined Herbal Properties for Distinguishing Prescriptions of Traditional Chinese Medicine from Non-Prescription Recipes C.Y. Ung, H. Li, C.Y. Kong, J.F. Wang and Y.Z. Chen. J. Ethnopharmacol. 109 (1): 21-28 (2006). Pubmed
  150. Tannic acid, a potent inhibitor of epidermal growth factor receptor tyrosine kinase Bin Yang E, Wei L, Zhang K, Chen YZ, Chen WN . J Biochem (Tokyo) 139(3):495-502 (2006). Pubmed
  151. Military vehicle classification via acoustic and seismic signals using statistical learning methods Xiao HG, Cai CZ, Chen YZ. Inter. J. Mod. Phys. C 17 (2): 197-212 FEB (2006).
  152. PROFEAT: A Web Server for Computing Structural and Physicochemical Features of Proteins and Peptides from Amino Acid Sequence. Z.R. Li, H.H. Lin, L.Y. Han, L. Jiang, X. Chen, and Y.Z. Chen. Nucleic Acids Res.Jul 1;34(Web Server issue):W32-7 (2006).Pubmed
  153. Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity. L.Y. Han, J. Cui, H.H. Lin, Z.L. Ji, Z.W. Cao, Y.S. Li, and Y.Z. Chen Proteomics. Vol.6: 4023-4037 (2006).Pubmed
  154. MHC-BPS: MHC-Binder Prediction Server for Identifying Peptides of Flexible Lengths from Sequence-Derived Physicochemical Properties. J. Cui, L.Y. Han, H.H. Lin, Z.Q. Tang, C.J. Zheng, Z.W. Cao, and Y.Z. Chen Immunogenetics 58(8):607-13 (2006) . Pubmed
  155. Application of Support Vector Machines to in silico Prediction of Cytochrome P450 Enzyme Substrates and Inhibitors. C. W. Yap, Y. Xue, Z. R. Li, and Y. Z. Chen Curr. Top. Med. Chem. 6(15):1593-1607 (2006).Pubmed
  156. Prediction of Estrogen Receptor Agonists and Characterization of Associated Molecular Descriptors by Statistical Learning Methods. H. Li, C. Y. Ung, C. W. Yap, Y. Xue, Z. R. Li and Y. Z. Chen. J. Mol. Graph. Mod. 25 (3): 313-323 (2006).Pubmed
  157. In silico Search of Putative Adverse Drug Reaction Related Proteins as a Potential Tool for Facilitating Drug Adverse Effect Prediction. Z. L. Ji, Y. Wang, L. Yu, L. Y. Han, C. J. Zheng, and Y. Z. Chen. Toxicol Lett. 164:104-112 (2006). Pubmed
  158. Information of ADME-associated proteins and potential application for pharmacogenetic prediction of drug responses.C.J. Zheng, L.Y. Han, X. Chen, Z.W. Cao, J. Cui, H.H. Lin, H.L. Zhang, H. Li and Y. Z. Chen. Curr. Pharmacogenomics. 4(2):87-103 (2006).
  159. Therapeutic Targets: Progress of Their Exploration and Investigation of Their Characteristics. C.J. Zheng, L.Y. Han, C. W. Yap, Z. L. Ji, Z. W. Cao and Y. Z. Chen. Pharmacol. Rev. 58:259-279(2006)Pubmed.
  160. Increasing the Odds of Drug Hit Identification by Screening Against Receptor Homologs? Z.L. Ji, Z.R. Li, J.F. Wang, C.Z. Cai, L.Y. Han, C.J. Zheng, and Y.Z. Chen. Lett. Drug. Des. Discov. 3(3):200-204 (2006)
  161. Progress and Difficulties in the Exploration of Therapeutic Targets C.J. Zheng, L.Y. Han, C. W. Yap, B. Xie, and Y. Z. Chen Drug Discov. Today 11(9-10):412-420 (2006)Pubmed
  162. Prediction of Compounds with Specific Pharmacodynamic, Pharmacokinetic or Toxicological Property by Statistical Learning Methods. C. W. Yap, Y. Xue, H. Li, Z. R. Li, C. Y. Ung, L. Y. Han, C. J. Zheng, Z. W. Cao and Y. Z. Chen. Mini. Rev. Med. Chem. 6:449-459 (2006)Pubmed
  163. Prediction of the Functional Class of Lipid-Binding Proteins from Sequence Derived Properties Irrespective of Sequence Similarity. H.H. Lin, L.Y. Han, H.L. Zhang, C.J. Zheng, B. Xie, and Y.Z. Chen. J. Lipid Res. 47(4):824-31 (2006)Pubmed
  164. PEARLS: Program for Energetic Analysis of Receptor-Ligand System. L.Y. Han, H.H. Lin, Z. R. Li, C.J. Zheng, Z.W. Cao, B. Xie, and Y. Z. Chen. J. Chem. Inf. Model. 23(1):445-450 (2006)Pubmed
  165. Traditional Chinese Medicine Information Database. Z. L. Ji, H. Zhou, J. F. Wang, L. Y. Han, C. J. Zheng, and Y. Z. Chen. J. Ethnopharmacol. 103(3):501 (2006).Pubmed
  166. Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic or toxicological properties of pharmaceutical agents. H. Li, C. W. Yap, Y. Xue, Z. R. Li, C. Y. Ung, L. Y. Han, and Y. Z. Chen Drug Dev. Res. 66:245-259(2006)
  167. Prediction of Transporter Family by Support Vector Machine Approach H. H. Lin, L.Y. Han, C.Z. Cai, Z. L. Ji, and Y.Z. Chen. Proteins. 62 (1): 218-31 (2006)Pubmed
  168. Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods. C. W. Yap, Z. R. Li, and Y. Z. Chen. J. Mol. Graph. Mod 24 (5): 383-395 (2006).Pubmed
  169. Prediction of Putative Adverse Drug Reaction-Related Proteins from Primary Sequence by Support Vector Machines. Z. L. Ji, L. Y. Han, C. J. Zheng,Z.W. Cao and Y. Z. Chen. Int. J. of Pharm. Med. 19(5-6):317-322 (2005) .
  170. Prediction of Functional Class of Novel Bacterial Proteins without the Use of Sequence Similarity by a Statistical Learning Method.J. Cui, L. Y. Han, C. Z. Cai, C.J.Zheng, Z. L. Ji, and Y. Z. Chen.J. Mol. Microbiol. Biotech. 9 (2): 86-100 (2005)
  171. Prediction of Functional Class of the SARS Coronavirus Proteins by a Statistical Learning Method.C.Z. Cai, L.Y. Han,X.Chen,Z.W. Cao,Y.Z. Chen. J. Proteome Res. 4 (5): 1855-1862 (2005).
  172. Effect of Selection of Molecular Descriptors on the Prediction of Blood-Brain Barrier Penetrating and Non-penetrating Agents by Statistical Learning Methods. H. Li, C. W. Yap, C. Y. Ung,Y. Xue, Z. W. Cao, and Y. Z. Chen. J. Chem. Inf. Model. 45 (5): 1376-1384 (2005).
  173. Prediction of Functional Class of Novel Plant Proteins by a Statistical Learning Method. L. Y. Han, C. J. Zheng, H. H. Lin, J. Cui, H. Li, H. L. Zhang, Z. Q. Tang, and Y. Z. Chen, New Phytologist. 168:109-121(2005)
  174. Assessment of approximate string matching in a biomedical text retrieval problem . J.F. Wang, Z.R.Li,C.Z.Cai, and Y. Z. Chen.Comput. Biol. Med. 35(8): 717-724 (2005).
  175. TCM-ID: Traditional Chinese Medicine information database. J. F. Wang, H. Zhou, L. Y. Han, Z.W. Cao , X. Chen and Y. Z. Chen,Clin. Pharmacol. Ther.. 78(1):92-93 (2005).
  176. Prediction of Cytochrome P450 3A4, 2D6, 2C9 Inhibitors and Substrates by Using Support Vector Machines. C.W. Yap, Y.Z.Chen J. Chem. Inf. Model. 45(4): 982-992 (2005).
  177. Prediction of Genotoxicity of Chemical Compounds by Statistical Learning Methods. H. Li, C. Y. Ung, C. W. Yap, Y. Xue, Z. R. Li, Z. W. Cao, and Y. Z. Chen. Chem Res Toxicol.18(6):1071-1080 (2005).
  178. A Computer Method for Validating Traditional Chinese Medicine Herbal Prescriptions. J. F. Wang,C. Z. Cai, C. Y. Kong, Z. W. Cao and Y. Z. Chen. Am. J. Chin. Med.33(2):281-297(2005).
  179. Trends in Exploration of Therapeutic Targets C.J. Zheng, L.Y. Han, C. W. Yap, B. Xie, and Y. Z. Chen, Drug News Perpect. 18(2):109-127 (2005)
  180. Computer prediction of drug resistance mutations in proteins. Z. W. Cao, L. Y. Han, C. J. Zheng, Z. L. Ji, X. Chen, H. H. Lin and Y. Z. Chen Drug Discov. Today 10(7):521-529 (2005)
  181. Effect of training datasets on support vector machine prediction of protein-protein interactions S.L. Lo, C. Z. Cai, Y.Z. Chen and Maxey C. M. Chung.Proteomics. 5(4):876-884 (2005).
  182. Prediction of Functional Class of Novel Viral Proteins by a Statistical Learning Method Irrespective of Sequence Similarity. L.Y.Han, C.Z Cai, Z. L. Ji, Y.Z. Chen. Virology 331(1):136-143 (2005).
  183. Quantitative structure-pharmacokinetic relationships for drug distribution properties by using general regression neural network. C. W. Yap, Y.Z.Chen J Pharm Sci 94(1):153-168 (2005).
  184. Predicting Functional Family of Novel Enzymes Irrespective of Sequence Similarity: A Statistical Learning Approach. L.Y.Han, C.Z.Cai, Z.L.Ji, Z.W.Cao,J.Cui, Y.Z.Chen Nucleic Acids Res.32(21): 6437-6444(2004).
  185. Drug ADME-Associated Protein Database as a Resource for Facilitating Pharmacogenomics Research. C.J. Zheng, L. Z. Sun, L. Y. Han, Z. L. Ji, X. Chen, and Y. Z. Chen. Drug. Dev. Res. 62:134–142 (2004).
  186. Density Functional Theory Studies on Structure, Spectra, and Electronic Properties of 3,7-Dinitrodibenzobromolium Cation and Chloride.X. H., Y. P. Feng,Y. Z. Chen.J. Phys. Chem. A, 108:7596-7602 (2004).
  187. TRMP: A Database of Therapeutically Relevant Multiple-Pathways. C.J.Zheng, H. Zhou, B. Xie, L.Y. Han, C.W. Yap, and Y. Z. Chen, Bioinformatics. 20(14):2236-41(2004)
  188. Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents.Xue, Y.; Li, Z. R.; Yap, C. W.; Sun, L. Z.; Chen, X.; Chen, Y. Z. J.Chem. Inf. Comput. Sci. 44,1630-1638(2004)
  189. Prediction of p-glycoprotein substrates by support vector machine approach.Xue, Y.; Yap, C. W.; Sun, L. Z.; Cao, Z. W.; Wang, J. F.; Chen, Y. Z. J.Chem. Inf. Comput. Sci. 44(4), 1497-505 (2004)
  190. MoViES: Molecular Vibrations Evaluation Server for Analysis of Fluctuational Dynamics of Proteins and Nucleic Acids. Z.W. Cao, Y. Xue, L.Y. Han, B. Xie, H. Zhou, C.J. Zheng, H.H. Lin and Y. Z. Chen, Nucleic Acids Res.32(Web Server Issue),W679-W685. (2004).
  191. Prediction of torsade-causing potential of drugs by support vector machine approach.Yap, C. W., Cai, C. Z., Xue, Y., and Chen, Y. Z. Toxicol. Sci. 79(1),170-177. (2004).
  192. Enzyme Family Classification by Support Vector Machines.C.Z. Cai, L.Y. Han, Z.L. Ji, Y.Z. Chen .Proteins. 55,66-76 (2004).
  193. Prediction of RNA-Binding Proteins from Primary Sequence by Support Vector Machine Approach.L.Y. Han, C.Z. Cai, S. L. Lo, Maxey C. M. Chung,Y. Z. Chen. RNA. 10(3),355-368. (2004).
  194. Advances in modeling of biomolecular interactions.C.Z.Cai, Z.R. Li, W. L. Wang, Y.Z. Chen. Acta Pharmacol. Sin.25,1-8(2004).
  195. Support Vector Machine Classification of Physical and Biological Datasets.C.Z. Cai, W.L.Wang, and Y.Z.Chen. Inter.J.Mod.Phys.C 14(5),575 - 585. (2003).
  196. Protein function classification via support vector machine approach.C.Z. Cai ,W.L. Wang, L.Z. Sun, Y.Z. Chen. Math Biosci, 185, 111-122 (2003).
  197. Can an In-Silico Drug-Target Search Method be Used to Probe Potential Mechanisms of Medicinal Plant Ingredients? X. Chen, C. Y. Ung, and Y. Z. Chen. Nat. Prod. Rep., 20, 432 - 444 (2003).
  198. DART: Drug Adverse Reaction Target Database. Z. L. Ji, L. Y. Han, Chun Wei Yap, Li Zhi Sun, Xin Chen, and Yu Zong Chen .Drug Safety 26(10), 685-690 (2003).
  199. SVM-Prot: Web-Based Support Vector Machine Software for Functional Classification of a Protein from Its Primary Sequence.C.Z. Cai, L.Y. Han, Z.L. Ji, X. Chen, Y.Z. Chen. Nucleic Acids Res., 31(13),3692-3697. (2003).
  200. Calculation of Free Energy of the Integrable Landau-Lifshitz Model. C.Z. Cai, W.L. Wang, Y.Z. Chen. Chin. Phys. Lett., 20(7),1009-1012(2003).
  201. Internet Resources for Proteins Associated with Drug Therapeutic Effects, Adverse Reactions, and ADME. Z. L. Ji, L. Z. Sun, X. Chen, C. J. Zheng, L. X. Yao, L. Y. Han, Z.W. Cao, J. F. Wang, W. K. Yeo, C.Z. Cai, and Y. Z. Chen.Drug Discov. Today, 8(12),526-529. (2003).
  202. KDBI:Kinetic Data of Bio-molecular Interactions Database. Z. L. Ji, X. Chen, C. J. Zheng, L.X. Yao, L. Y. Han , W. K. Yeo, P. C. Chung, H. S. Puy, Y. T. Tay, A. Muhammad, and Y. Z. Chen. Nucleic. Acids. Res., 31(1), 255-257. (2003).
  203. Correlation between Normal Modes in The 20-200cm-1 Frequency Range and Localized Torsion Motions Related to Certain Collective Motions in Proteins. Z. W. Cao, X. Chen and Y. Z. Chen.J. Mol. Graph. Mod. 21,309-319. (2003).
  204. ADME-AP: A database of ADME associated proteins.L. Z. Sun, Z. L. Ji, X. Chen, J. F. Wang, and Y. Z. Chen. Bioinformatics., 18,1699-1700 . (2002).
  205. CLiBE: A Database of Computed Ligand Binding Energy for Ligand-Receptor Complexes and Its Potential Use in the Analysis of Drug Binding Competitiveness. X. Chen, Z. L. Ji, D.G. Zhi, and Y. Z. Chen, Comput. Biol. Chem. 26, 661-666. (2002).
  206. Computational Method for Drug Target Search and Application in Drug Discovery. Y. Z. Chen, Z. R. Li and C. Y. Ung, J. Theor. Comp. Chem., 1, 213-224. (2002).
  207. Absorption, distribution, metabolism, and excretion-associated protein database . L. Z. Sun, Z. L. Ji, X. Chen, J. F. Wang, and Y. Z. Chen,, Clin. Pharmacol. Ther. , 71, 405 (2002).
  208. Computer Automated Prediction of Putative Therapeutic and Toxicity Protein Targets of Bioactive Compounds from Chinese Medicinal Plants. Y. Z. Chen and C. Y. Ung, Am. J. Chin. Med., 30, 139-154. (2002).
  209. TTD: Therapeutic Target Database. X. Chen, Z.L. Ji, and Y. Z. Chen, Nucleic. Acids. Res., 30, 412-415 (2002).
  210. Inhibition of epidermal growth factor receptor (EGFR) tyrosine kinase by chalcone derivatives. E. B. Yang, Y. J. Guo, K Zhang, Y.Z. Chen, and P. Mack, BBA: Prot. Struct. Mol. Enzym.  1550, 144-152 (2001).
  211. Prediction of Potential Toxicity and Side Effect Protein Targets of a Small Molecule by a Ligand-Protein Inverse Docking Approach.Y. Z. Chen, C. Y. Ung, J. Mol. Graph. Mod., 20, 199-218 (2001).
  212. Can an optimization/scoring procedure in ligand-protein docking be employed to probe drug-resistant mutations in proteins? Y. Z. Chen, X. L. Gu, and Z. W. Cao, J. Mol. Graph. Mod. 19, 560-570 (2001).
  213. A computer approach for finding putative protein targets of a small molecule. Y. Z. Chen, FASEB J 15, A915 (2001).
  214. Application of computer-aided drug target search in probing molecular mechanism of bioactive Chinese natural products. Y. Z. Chen, and C. Y. Ung, Chin. J. Med. Chem. 11, 145-148 (2001).
  215. Ligand-Protein Inverse Docking and Its Potential Use in Computer Search of Putative Protein Targets of a Small Molecule. Y. Z. Chen and D. G. Zhi, Proteins, 43, 217-226 (2001).
  216. Hydrogen bond disruption probability in proteins by a modified selfconsistent harmonic approach. Z. W. Cao and Y. Z. Chen, Biopolymers, 58, 319-328 (2001).
  217. Computer search of putative protein targets of a small molecule. Y.Z. Chen, Biophys. J. 80, 497A (2001).
  218. Spontaneous base flipping in DNA and its possible role in methyltransferase binding.Y.Z. Chen, V. Mohan, and R. H. Griffey, Phys. Rev. E62, 1133-1137 (2000).
  219. Base Opening in RNA and DNA duplexes: Implication for RNA stability.Y.Z. Chen, V. Mohan, and R. H. Griffey, Phys. Rev. E61, 5640-5645 (2000).
  220. Modified self-consistent harmonic approach to thermal fluctuational disruption of disulfide bonds in proteins. Y.Z. Chen, Phys. Rev. E60, 5938-5942 (1999).
  221. Molecular basis for the stereospecificity of Candida rugosa Lipase (CRL) towards ibuprofen. B. S. Lakshmi, P. Kangueane, Y. Guo., Y. Z. Chen, and P. Gautam., Biocatal Biotransfor 17, 475-486 (1999).
  222. Principal Torsion Angles of Collective Motions in Biomolecules: A Study on Single Base Opening in DNA Duplexes. Y.Z. Chen, V. Mohan, and R.H. Griffey, Phys. Rev. E58, 909-913 (1998).
  223. Effect of backbone zeta torsion angle on low energy single base opening in B-DNA crystal structures. Y.Z. Chen, V. Mohan, and R.H. Griffey, Chem. Phys. Lett. 287, 570 (1998).
  224. Drug binding to DNA: Observation of the drug-DNA hydrogen-bond-stretching modes of netropsin bound to DNA via Raman spectroscopy. S.A. Lee, A. Rupprecht, and Y.Z. Chen, Phys. Rev. Lett. 80, 2241 (1998).
  225. The opening of a single base without perturbations of neighboring nucleotides: A study on crystal B-DNA duplex d(CGCGAATTCGCG)2. Y.Z. Chen, V. Mohan, and R.H. Griffey, J. Biomol. Struct. Dyn. 15, 765 (1998).
  226. Recent results on the statistical mechanis of intergrable models in 1+1 dimensions. Y.Z Chen, R. K. Bullough, D. J. Pilling and J. Timonon. World aquaculture. (1998)
  227. Theory of DNA melting based on the Peyrard-Bishop model. Y.L. Zhang, W.M. Zheng, J.X. Liu, Y.Z. Chen, Phys. Rev. E56, 7100-7115 (1997).
  228. A general random walk model of ATP-driven helicase translocation along DNA. Y.Z. Chen, Dong Mi, He-Shang Song, and Xian-Ju Wang, Phys. Rev. E56, 919 (1997).
  229. Effect of drug binding induced deformation on the vibrational spectrum of a daunomycin-DNA complex. Y. Z. Chen, J.W. Powell, S.A. Lee and E.W. Prohofsky, Phys. Rev. E55, 7414 (1997).
  230. Calculation of the binding affinity of the anticancer drug daunomycin to DNA by a statistical mechanics approach. Y.Z. Chen, and Yong-Li Zhang, Phys. Rev. E55, 7390 (1997).
  231. Binding stability of a cross-linked drug: Calculation of an anticancer drug cisplatin-DNA complex. Y.Z. Chen, E.W. Prohofsky, and Yong-Li Zhang, Phys. Rev. E55, 5843 (1997).
  232. Vibrational Analysis of Phosphrothioate DNA: II. The POS group in the model compound dimethyl phosphorothioate [CH_3O)_2(POS)]-. C.A. Steinke, K.K. Reeves, J.W. Powell, S.A. Lee, Y.Z. Chen, T. Wyrzykiewicz, R.H. Griffey, and V. Mohan. J. Biomol. Struct. Dyn. 14, 509 (1997).
  233. Vibrational normal modes and dynamical stability of DNA triplex Poly(dA)-2Poly(dT): S-type structure is more stable and in better agreement with observations in solution. Y.Z. Chen, J.W. Powell, and E.W. Prohofsky. Biophys. J.72, 1327 (1997).
  234. Sequence dependent DNA melting predicted by a microscopic statistical theory. Y. Z. Chen and E.W. Prohofsky, Eur. Biophys. J. 25, 9 (1996).
  235. Melting profile and temperature dependent binding constant of an anticancer drug daunomycin-DNA complex. Y.Z. Chen and E.W. Prohofsky, Eur. Biophys. J. 24, 203 (1996).
  236. Stability and conformation of [d(T)-d(A)-d(T)](n). Y.Z. Chen and E.W. Prohofsky, Biophys. J. 70, SUPM7 (1996).
  237. Calculation of the dynamics of drug binding in a netropsin-DNA complex. Y. Z. Chen and E.W. Prohofsky, Phys. Rev. E51, 5048 (1995).
  238. Normal mode calculation of a netropsin-DNA complex: Effect of structural deformation on vibrational spectrum. Y. Z. Chen and E.W. Prohofsky, Biopolymers 35, 657 (1995).
  239. Sequence and temperature dependence of the interbase hydrogen bond breathing modes in B-DNA polymers. Comparison with low frequency Raman peaks and their role in helix melting. Y. Z. Chen and E.W. Prohofsky, Biopolymers35, 573 (1995).
  240. Nonlinear effects and thermal expansion as expressed in selfconsistent phonon calculations on the temperature dependence of a phase change: Application to the B to Z conformation change in DNA. Y.Z. Chen and E.W. Prohofsky, Phys. Rev. E49, 3444 (1994).
  241. Premelting base pair opening probability and drug binding constant of a daunomycin--Poly d(GCAT)-Poly d(ATGC) complex. Y.Z. Chen and E.W. Prohofsky, Biophys. J. 66, 820 (1994).
  242. Near-neighbor effects in cooperative modified selfconsistent phonon approximation melting in DNA. Y.Z. Chen and E.W. Prohofsky, Phys. Rev. E 49, 873 (1994).
  243. First or second order transition in the melting of repeat sequence DNA. Y.Z. Chen and E.W. Prohofsky,Biophys. J.66, 202 (1994).
  244. The role of thermal flucatuational base pair disruption in DNA-B to DNA-Z conformation change. Y.Z. Chen and E.W. Prohofsky,Biophys. J.66, A291 (1994).
  245. Theoretical study of the effect of salt and the role of strained hydrogen bonds on the thermal stability of DNA polymers. Y.Z. Chen & E.W. Prohofsky, Phys. Rev. E48, 3099 (1993).
  246. Salt dependent premelting base pair opening probabilities of B and Z DNA Poly[d(G-C)] and significance for the B-Z transition. Y.Z. Chen & E.W. Prohofsky, Biophys. J. 64, 1394 (1993).
  247. Synergistic effects in the melting of DNA hydration shell: Melting of the minor groove hydration spine in Poly(dA)-Poly(dT) and its effect on base pair stability. Y.Z. Chen & E.W. Prohofsky,Biophys. J. 64, 1385 (1993).
  248. Differences in melting behavior between homopolymers and copolymers of DNA: Role of non-bonded forces for GC and the role of the hydration spine and premelting transition for AT. Y.Z. Chen & E.W. Prohofsky, Biopolymers33, 797 (1993).
  249. A cooperative self-consistent microscopic theory of thermally induced melting of a repeat sequence DNA polymer. Y.Z. Chen & E.W. Prohofsky, Biopolymers33, 351 (1993).
  250. Theory of presure-dependent melting of the DNA double helix: Role of strained hydrogen bonds. Y.Z. Chen & E.W. Prohofsky, Phys. Rev. E47, 2100 (1993).
  251. Strained hydrogen-bonds between Poly(dA).Poly(dT) and its hydration spin and its role in base pair stability. Y.Z. Chen and E.W. Prohofsky,Biophys. J.64, A356 (1993).
  252. Description of base motion and role of excitations in the melting of Poly(dG)-Poly(dC). W. Zhuang, Y.Z. Chen & E.W. Prohofsky, J. Biomol. Struc. Dyn. 10, 403 (1992).
  253. Energy flow considerations and thermal fluctuational opening of DNA base pairs at a replicating fork: Unwinding consistent with observed replication rates. Y.Z. Chen, W. Zhuang & E.W. Prohofsky, J. Biomol. Struct. Dynam. 10, 415 (1992).
  254. Salt-dependent stability of Poly(dG)-Poly(dC) with potential of mean force Coulomb interactions. Y.Z. Chen, W. Zhuang & E.W. Prohofsky, Biopolymers32, 1123 (1992).
  255. The role of a minor groove spine of hydration in stabilizing Poly(dA)-Poly(dT) against fluctuational interbase H-bond disruption in the premelting temperature regime. Y.Z. Chen & E.W. Prohofsky,Nucleic. Acids. Res. 20, 415 (1992).
  256. Premelting thermal fluctuational interbase hydrogen bond disrupted states of a B DNA Guanine-Cytosine base pair. Significance for amino and imino proton exchange. Y.Z. Chen, W. Zhuang & E.W. Prohofsky, Biopolymers31, 1273 (1991).
  257. Premelting thermal fluctuational base pair opening probability of Poly(dA)-Poly(dT) as predicted by the modified self-consistent phonon theory. Y.Z. Chen, Y. Feng & E.W. Prohofsky, Biopolymers 31, 139 (1991).
  258. A selfconsistent mean-field calculation of a homopolymer DNA strand separation: bond breaking in a macromolecule. Y.Z. Chen & E.W. Prohofsky, J. Chem. Phys.94, 4665 (1991).
  259. Criterion of thermal denaturation for modified-self-consistent-phonon-theory mean-field calculations in DNA polymers. Y.Z. Chen, Y. Feng & E.W. Prohofsky, Phys. Rev. B42, 11335 (1990).


Publications in the Area of Nonlinear Science
  1. Thermodynamics of Toda lattice models: application to DNA. R.K. Bullough, Y.Z. Chen and J.Timonen, Physica D 68, 83 (1993).
  2. Order and chaos in the statistical mechanics of the integrable models in 1+1 dimensions. R.K. Bullough, Y.Z. Chen and J.Timonen, in: Proc. Intl Meeting `Aspects of Nonlinear Dynamics and solitons and Chaos', eds. I. Antonin and F.J. Lambert, Springer-Verlag, Heidelberg (1991). pp. 25-37.
  3. Exact results in the statistical mechanics of integrable models in (1+1) dimensions. R.K. Bullough, D.J. Pilling, Y.Z. Chen & J. Timonon, in: Exact results in quantum dynamics, eds. J. Dittrich et. al., World Scientific, Singapore (1991).
  4. Nonlinearity and disorder in the statistical mechanics of integrable systems. R.K. Bullough, J. Timonon & Y.Z. Chen, in: Nonlinearity and disorder, eds. A.R. Bishop et. al., Springer-Verlag, Berlin (1991).
  5. Classical thermodynamics of the Heisenberg chain in a field by generalized Bethe ansatz method. R.K. Bullough, Y.Z. Chen, J. Timonon, V. Tognetti & R. Vaia, Phys. Lett. A145, 154 (1990).
  6. Quantum and classical statistical mechanics of the integrable models. R.K. Bullough, Y.Z. Chen, Y. Cheng, D.J. Pilling & J. Timonon, in: Nonlinear evolution equations: integrability and spectral methods, eds. A. Degasperis, A.D. Fordy & M. Lakshmanan. (Manchester Univ. Press, Manchester 1990).
  7. Soliton statistical mechanics -- thermodynamic limits for quantum and classical integrable models. R.K. Bullough, Y.Z. Chen & J. Timonon, in: Nonlinear and turbulent processes in physics, eds. V.G. Baryakhtar, V.M. Chernousenko, N.S. Erokhin & V.E. Zakharov. (World Scientific, Singapore, 1990). pp.1377-1422.
  8. Soliton statistical-mechanics and the thermalization of biological solitons. R.K. Bullough, D.J. Pilling, Y. Cheng, Y.Z. Chen and J. Timonen, , J. Phys. (Paris) 50 (Suppl 3), 41-51 (1989).
  9. Statistical mechanics of the NLS models and their avartars. R.K. Bullough, Y.Z. Chen, S. Olafsson & J. Timonon. in: Integrable systems and applications, eds. M. Balabane, P. Lochak & C. Sulem. (Springer-Verlag, Heidelberg,1989). 12-26.
  10. Soliton statistical mechanics and thermalization of biological solitons. R.K. Bullough, D.J. Pilling, Y. Cheng, Y.Z. Chen & J. Timonon, in: Nonlinear coherent structures in physics, mechanics and biological systems, ed. J. Puget. (E.S.P.C.I (Paris) les editions de physique, Les Uhs, France 1989).
  11. Statistical mechanics of integrable models J. Timonon, Y.Z. Chen & R.K. Bullough, Nucl. Phys. B 5A, 58 (1988).
  12. Recent results on the statistical mechanics of integrable models in 1+1 dimensions. R.K. Bullough, Y.Z. Chen, D.J. Pilling & J. Timonon, in: Plasma theory and nonlinear and turbulent processes in physics, eds. V.G. Bar'yakhtar, V.M. Chernousenko, N.S. Erokhin, A.G. Sitenko & V.E. Zakharov (World Scientific, Singapore, 1988).