LiRong Wang

Assistant Professor

Dr. Lirong Wang is an Assistant Professor of Pharmaceutical Sciences, Computational Chemical Genomics Screening (CCGS) Center (, School of Pharmacy.

Dr. Wang obtained his PhD degree from University of Science and Technology of China. He then received postdoctoral training in Dr. Xiang-Qun Xie group at the School of Pharmacy, University of Pittsburgh.

His research mainly focuses on developing and applying machine/deep learning algorithms for outcome related research with electronic medical records, chemogenomics/chemoinformatics algorithm and online tools development. He is the author or coauthor of more than 60 journal articles and he has designed a lot of tools, such as DeepBiomarker, TargetHunter, BBB Predictor and HTDocking.

Dr. Wang is the (M)PI of R01 R01MH116046,  and has or had been a co-investigator or key personal in the NIH funded projects P30 PDA035778A, R01 NLM 015417, R01 DA025612, NIGMP50 UPCMLD project and NCI UP-CDC project. Dr. Wang has served as ad hoc reviewer for AAPS Journal, JCIM, Bioinformatics, JMC, Journal of Cheminformatics, Journal of Personalized Medicine, Journal of Clinical Medicine, Molecules, etc. Dr. Wang is an Associate Editor for Medicine and Public Health(specialty section of Frontiers in Big Data and Frontiers in Artificial Intelligence).

    Education & Training

  • Dr. Wang obtained his PhD degree from University of Science and Technology of China.
Research Interests

Deep/machine learning, electronic medical records analysis, clinical trial emulation, target identification, chemogenomics/chemoinformatics/bioinformatics database,  signaling pathway, clinical data mining on mental disorders like substance abuse disorder, Alzheimer's Disease, TBI, PTSD, suicide and psychosis.

Recent Publications
  1. Miranda O, Fan P, Qi X, Wang H, Brannock MD, Kosten TR, Ryan ND, Kirisci L, Wang L. DeepBiomarker2: Prediction of alcohol and substance use disorder risk in post-traumatic stress disorder patients using electronic medical records and multiple social determinants of health. Journal of Personalized Medicine. 2024;14(1):94.
  2. Miranda O, Fan P, Qi X, Wang H, Brannock MD, Kosten T, Ryan ND, Kirisci L, Wang L. Prediction of Adverse Events Risk in Patients with Comorbid Post-Traumatic Stress Disorder and Alcohol Use Disorder Using Electronic Medical Records by Deep Learning Models. Drug and Alcohol Dependence. 2024:111066.
  3. Fan P, Miranda O, Qi X, Kofler J, Sweet RA, Wang L. Unveiling the Enigma: Exploring Risk Factors and Mechanisms for Psychotic Symptoms in Alzheimer’s Disease through Electronic Medical Records with Deep Learning Models. Pharmaceuticals. 2023;16(7):911.
  4. Krivinko J, DeChellis-Marks M, Zeng L, Fan P, Lopez O, Ding Y, Wang L, Kofler J, MacDonald M, Sweet R. Targeting the post-synaptic proteome has therapeutic potential for psychosis in Alzheimer Disease. Communications Biology. 2023;6(1):598.
  5. Fan P, Zeng L, Ding Y, Julia Kofler, Jonathan Silverstein, Joshua Krivinko, Robert Sweet, Lirong Wang. Combination of Antidepressants and Antipsychotics as A Novel Treatment Option for Psychosis in Alzheimer’s Disease. CPT: Pharmacometrics & Systems Pharmacology.; 01 May 2023
  6. Fan, P., Julia Kofler, Ying Ding, Michael Marks, Robert A Sweet, Lirong Wang. Efficacy difference of antipsychotics in Alzheimer’s disease and schizophrenia: explained with network efficiency and pathway analysis methods, Briefings in Bioinformatics, 2022; bbac394.
  7. Miranda O, Fan P, Qi X, Yu Z, Ying J, Wang H, Brent D, Silverstein J, Chen Y, Wang L. “DeepBiomarker: Identifying Important Lab Tests from Electronic Medical Records for the Prediction of Suicide-related Events among PTSD Patients”. J. Pers. Med. 2022, 12(4), 524
  8. Gray M, Priyanka P, Kane-Gill S, Wang L, Kellum JA. “Kidney and Mortality Outcomes Associated with Ondansetron in Critically Ill Patients” J Intensive Care Med 2022 Jan 8;8850666211073582.
  9. Xiaojiang Guo, Xiguang Qi, Peihao Fan, Michael Gilbert, Andrew D. La, Zeyu Liu, Richard Bertz, John A. Kellum, Yu Chen, Lirong Wang, “Effect of Ondansetron on Reducing ICU Mortality in Patients with Acute Kidney Injury”, Scientific Reports, 11, 19409 (2021)
  10. Noah R Delapaz, William K Hor, Michael Gilbert, Andrew D La, Feiran Liang, Peihao Fan, Xiguang Qi, Xiaojiang Guo, Jian Ying, Dara Sakolsky, Levent Kirisci, Jonathan C Silverstein, Lirong Wang, “An emulation of randomized trials of administrating antipsychotics in PTSD patients for outcomes of suicide-related events”, Journal of personalized medicine 11 (3), 178, 2021
  11. Fan, P., Lirong Wang, et al., Prediction of suicide-related events by analyzing electronic medical records from PTSD patients with bipolar disorder. Brain sciences, 2020. 10(11): p. 784.
  12. Gilbert, M., Lirong Wang, et al., An emulation of randomized trials of administrating benzodiazepines in PTSD patients for outcomes of suicide-related events. 2020. 9(11): p. 3492.
  13. Xiguang Qi, Mingzhe Shen, Peihao Fan, Xiaojiang Guo, Tianqi Wang, Ning Feng, Manling Zhang, Robert A. Sweet*, Levent Kirisci*, and Lirong Wang*, The Performance of Gene Expression Signature-Guided Drug-Disease Association in Different Categories of Drugs and Diseases, Molecules(2020).
  14. P. Fan, X. Qi, R.A. Sweet*, L. Wang*, Network Systems Pharmacology-Based Mechanism Study on the Beneficial Effects of Vitamin D against Psychosis in Alzheimer’s Disease, Scientific Reports, 10 (2020) 1-13.
  15. Z. Hu, Y. Jing, Y. Xue, P. Fan, L. Wang, M. Vanyukov, L. Kirisci, J. Wang, R.E. Tarter, X.-Q. Xie, Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity, Drug and alcohol dependence, 206 (2020) 107604.
  16. Y. Jing, Z. Hu, P. Fan, Y. Xue, L. Wang, R.E. Tarter, L. Kirisci, J. Wang, M. Vanyukov, X.-Q. Xie, Analysis of substance use and its outcomes by machine learning I. Childhood evaluation of liability to substance use disorder, Drug and alcohol dependence, 206 (2020) 107605.
  17. Bian Y, Jing Y, Wang L, Ma S, Jun JJ, Xie XQ. “Prediction of Orthosteric and Allosteric Regulations on Cannabinoid Receptors Using Supervised Machine Learning Classifiers.” Mol Pharm. 2019 Jun 3;16(6):2605-2615. doi: 10.1021/acs.molpharmaceut.9b00182. Epub 2019 May 3.
  18. Wu X, Xie S, Wang L, Fan P, Ge S, Xie XQ, Wu W. “A computational strategy for finding novel targets and therapeutic compounds for opioid dependence” PLoS One. 2018 Nov 7;13(11):e0207027. doi: 10.1371/journal.pone.0207027. eCollection 2018.
  19. Wang L, Ying J, Fan P, Weamer EA, DeMichele-Sweet MAA, Lopez OL, Kofler JK, Sweet RA “Effects of Vitamin D Use on Outcomes of Psychotic Symptoms in Alzheimer Disease Patients”, Am J Geriatr Psychiatry. 2019 Mar 27. pii: S1064-7481(19)30321-5. doi: 10.1016/j.jagp.2019.03.016
  20. Chen M, Jing Y, Wang L, Feng Z, Xie XQ. “DAKB-GPCRs: An Integrated Computational Platform for Drug Abuse Related GPCRs”. J Chem Inf Model. 2019 Apr 22;59(4):1283-1289. doi: 10.1021/acs.jcim.8b00623. Epub 2019 Mar 14.
  21. Fan P, Wang N, Wang L*, Xie XQ*. “Autophagy And Apoptosis Specifc Knowledgebases-Guided Systems Pharmacology Drug Research”. Curr Cancer Drug Targets. 2019 Feb 6. doi: 10.2174/1568009619666190206122149. [Epub ahead of print]
  22. Lirong Wang#, Shifan Ma#, Ziheng Hu, Terence Francis  McGuire, and Xiang-Qun Xie, “Chemogenomics Systems Pharmacology Mapping of Potential Drug Targets for Treatment of Traumatic Brain Injury” J Neurotrauma. 2019 Feb 15;36(4):565-575. doi: 10.1089/neu.2018.5757. Epub 2018 Sep 6.
  23. Hu Z, Wang L, Ma S, Kirisci L, Feng Z, Xue Y, Klunk WE, Kamboh MI, Sweet RA, Becker J, Lv Q, Lopez OL, Xie XQ “Synergism of antihypertensives and cholinesterase inhibitors in Alzheimer's disease”. Alzheimers Dement (N Y). 2018 Oct 14;4:542-555. doi: 10.1016/j.trci.2018.09.001. eCollection 2018
  24. Bian Y, Hu Z, Wang L, Xie XS. “Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era”. AAPS J. 2018 Mar 30;20(3):58. doi: 10.1208/s12248-018-0210-0.
  25. Yuemin Bian, Xibing He, Yankang Jing, Lirong Wang, Junmei Wang, Xiang-Qun Xie, “Computational systems pharmacology analysis of cannabidiol: a combination of chemogenomics-knowledgebase network analysis and integrated in silico modeling and simulation” Acta Pharmacologica Sinica, in press
  26. Nanyi Wang, Lirong Wang* and Xiang-Qun Xie*, “ProSelection: A novel algorithm to select proper protein structure subsets for in silico target identification and drug discovery research” J Chem Inf Model. 2017 Nov 27;57(11):2686-2698. doi: 10.1021/acs.jcim.7b00277. Epub 2017 Oct 26.
  27. Yu Zhang* Lirong Wang*, Haizi Cheng, Yahui Ding, Zhiwei Feng, Tao Cheng, Yingdai Gao and Xiang-Qun Xie* “StemCellCKB: An Integrated Stem Cell-Specific Chemogenomics Knowledge Base for Target Identification and Systems-Pharmacology Research” J. Chem. Inf. Model., 2016, 56 (10), pp 1995–2004
  28. Xiaomeng Xu, Shifan Ma, Zhiwei Feng, Guanxing Hu, Lirong Wang*, and Xiang-Qun Xie* “Chemogenomics Knowledgebase and Systems Pharmacology for Hallucinogen Target Identification - Salvinorin A as a Case Study” J MOL GRAPH MODEL , Volume 70, November 2016, Pages 284–295
  29. Hai Zhang, Shifan Ma, Zhiwei Feng, Dongyao Wang, Chengjian Li, Yan Cao, Xiaofei Chen, Aijun Liu, Zhenyu Zhu, Junping Zhang, Guoqing Zhang, Yifeng Chai*, Lirong Wang*, and Xiang-Qun Xie* “Disease-Specific Chemogenomics Knowledgebase-Guided Systems Pharmacology Approach for Target Identification and Drug Synergy and Antagonism Mechanism Study of A Combinational Herbal Formulations” Sci Rep. 2016 Sep 28;6:33963. doi: 10.1038/srep33963.
  30. Jianping Hu, Ziheng Hu, Yan Zhang,  Xiaojun Gou, Ying Mu, Lirong Wang* and Xiang-Qun Xie* “Metal binding mediated conformational change of XPA protein:a potential cytotoxic mechanism of Nickel in the nucleotide excision repair” J Mol Model. 2016 Jul;22(7):156. doi: 10.1007/s00894-016-3017-x. Epub 2016 Jun 16.
  31. Xiang-Qun Xie#, Lirong Wang# , Junmei Wang#, Zhaojun Xie,  Peng Yang, Qin Ouyang “In Silico Chemogenomics Knowledgebase and Computational System Neuropharmacology Approach for Cannabinoid Drug Research” DOI: 10.1016/B978-0-12-800634-4.00019-6 Chapter in book: Neuropathology of Drug Addictions and Substance Misuse, pp.183-195
  32. Zhang Z, Li HM, Zhou C, Li Q, Ma L, Zhang Z, Sun Y, Wang L, Zhang X, Zhu B, Hong YS, Wu CZ, Liu H. “Non-benzoquinone geldanamycin analogs trigger various forms of death in human breast cancer cells” J Exp Clin Cancer Res. 2016 Sep 22;35(1):149.
  33. Zhou A, Hu J, Wang L, Zhong G, Pan J, Wu Z, Hui A “Combined 3D-QSAR, molecular docking, and molecular dynamics study of tacrine derivatives as potential acetylcholinesterase (AChE) inhibitors of Alzheimer's disease” J Mol Model. 2015 Oct;21(10):277. doi: 10.1007/s00894-015-2797-8. Epub 2015 Oct 5.
  34. Yingdai Gao, Peng Yang, Hongmei Shen, Hui Yu, Xianmin Song, Liyan Zhang, Peng Zhang, Haizi Cheng, Zhaojun Xie, Sha Hao, Yahui Ding, Lirong Wang, Haibin Liu, Yanxin Li, Hui Cheng, Weimin Miao, Weiping Yuan, Youzhong Yuan, Tao Cheng, Xiang-Qun Xie “Small-molecule inhibitors targeting INK4 protein p18 INK4C enhance ex vivo expansion of haematopoietic stem cells”, 2015, Nature Communications  02/2015; 6. DOI: 10.1038/ncomms7328
  35. Qin Ouyang*, Lirong Wang*, Ying Mu and Xiang-Qun Xie “Modeling Skin Sensitization Potential of Mechanistically Hard-to-be-Classified Aniline and Phenol Compounds with Quantum Mechanistic Properties”, BMC Pharmacology and Toxicology 2014 (*co-first authors).
  36. Rentian Feng, Qin Tong, Zhaojun Xie, Lirong Wang, Suzanne Lentzsch, G. David Roodman, Charles Sfeir, and Xiang-Qun Xie, “Targeting Cannabinoid Receptor-2 Pathway by Phenylacetylamide Suppresses the Proliferation of Human Myeloma Cells Through Mitotic Dysregulation and Cytoskeleton Disruption” Mol Carcinog. 2015 Dec;54(12):1796-806. doi: 10.1002/mc.22251. Epub 2015 Jan 16.
  37. Feng, Z., M. H. Alqarni, P. Yang, Q. Tong, A. Chowdhury, L. Wang and X.-Q. Xie (2014). "Modeling, Molecular Dynamics Simulation and Mutation Validation for Structure of Cannabinoid Receptor 2 Based on Known Crystal Structures of GPCRs." J. Chem. Inf. Model., 2014, 54 (9), pp 2483–2499
  38. Liu, Haibin; Wang, Lirong; Su, Weiwei and Xiang-Qun Xie. (2014) Advances in recent patent and clinical trial drug development for Alzheimer's disease. Pharm Pat Anal 3:429-47
  39. Cai Z, Ouyang Q, Zeng D, Nguyen KN, Modi J, Wang L, White AG, Rogers BE, Xie XQ, Anderson CJ, “64Cu-labeled somatostatin analogues conjugated with cross-bridged phosphonate-based chelators via strain-promoted click chemistry for PET imaging: in silico through in vivo studies.” J Med Chem. 2014 Jul 24;57(14):6019-29. doi: 10.1021/jm500416f. Epub 2014 Jul 11.
  40. Shujing Sheng*, Jinxu Wang*, Lirong Wang*, Hong Liu, Peibo Li, Menghua Liu, Chaofeng Long, Chengshi Xie, Xiangqun Xie, Weiwei Su, “Network pharmacology Analyses of the Antithrombotic pharmacological mechanism of Fufang Xueshuantong capsule with experimental support using disseminated intravascular coagulation rats.” Journal of ethnopharmacology. 05/2014; DOI:10.1016/j.jep.2014.04.048(*these authors contributed equally).
  41. Liu H*, Wang L*, Lv M, Pei R, Li P, Pei Z, Wang Y, Su W, Xie X-Q “AlzPlatform: An Alzheimer's Disease Domain-specific Chemogenomics Knowledgebase for Polypharmacology and Target Identification Research”. Journal of Chemical Information and Modeling, DOI: 10.1021/ci500004h
    Publication Date (Web): March 5, 2014 (*these authors contributed equally).
  42. Lirong Wang, Xiang-Qun Xie "Computational target fishing: what should chemogenomics researchers expect for the future of in silico drug design and discovery?" Future Medicinal Chemistry, March 2014, Vol. 6, No. 3, Pages 247-249.
  43. Xiang-Qun Xie, Lirong Wang, Haibin Liu, Qin Ouyang, Cheng Fang, Weiwei Su “Chemogenomics knowledgebased polypharmacology analyses of drug abuse related G-protein coupled receptors and their ligands”. Frontiers in Pharmacology 2014; 5:3. DOI:10.3389/fphar.2014.00003
  44. Haibin Liu, Fengyin Liang,Weiwei Su,Ning Wang,Mingliang Lv,Peibo Li,Zhong Pei,Yan Zhang,Xiang-Qun Xie,Lirong Wang,Yonggang Wang, “Lifespan extension by n-butanol extract from seed of Platycladus orientalis in Caenorhabditis elegans”, J Ethnopharmacol. 2013 May 20;147(2):366-72. doi: 10.1016/j.jep.2013.03.019. Epub 2013 Mar 21.PMID: 23523941
  45. Matthew LaPorte , Sammi Tsegay , Ki Bum Hong , Chunliang Lu , Cheng Fang , Lirong Wang , Xiang-qun (Sean) Xie , and Paul E. Floreancig “Construction of a Spirooxindole Amide Library through Nitrile Hydrozirconation-Acylation-Cyclization Cascade”. ACS Comb Sci. 2013 Jun 3 PMID:23731121
  46. Yang P, Wang L, Feng R, Almehizia AA, Tong Q, Myint KZ, Ouyang Q, Alqarni MH, Wang L, Xie XQ. “Novel Triaryl Sulfonamide Derivatives as Selective Cannabinoid Receptor 2 Inverse Agonist and Osteoclast Inhibitor: Discovery, Optimization and Biological Evaluation.” J Med Chem. 2013 Mar 14;56(5):2045-58. doi: 10.1021/jm3017464. Epub 2013 Mar 1. PMID: 23406429
  47. Ma C*, Wang L*, Yang P, Tong Q, Myint KZ, and Xie XQ.  “LiCABEDS II. Modeling of Ligand Selectivity for G-protein Coupled Cannabinoid Receptors”, J Chem Inf Model. 2013 Jan 28;53(1):11-26. doi: 10.1021/ci3003914. Epub 2013 Jan 15 (*these authors contributed equally) PMID: 23278450
  48. Lirong Wang, Chao Ma, Peter Wipf, Haibin Liu, Weiwei Su and Xiang-Qun Xie. “TargetHunter: An In Silico Target Identification Tool for Predicting Therapeutic Potential of Small Organic Molecules Based on Chemogenomic Database”.  AAPS J. 2013 Apr;15(2):395-406. doi: 10.1208/s12248-012-9449-z. Epub 2013 Jan 5. PMID:23292636
  49. Manuj Tandon*, Lirong Wang*, Qi Xu, Xiang-Qun Xie, Peter Wipf* and Q. Jane Wang*. “A Targeted Library Screen Reveals a New Selective Inhibitor Scaffold for Protein Kinase D”.  Plos One 2012;7(9):e44653. doi: 10.1371/journal.pone.0044653 (*these authors contributed equally) PMID: 23028574
  50. Yang P, Myint KZ, Tong Q, Feng R, Cao H, Almehizia AA, Hamed AM, Wang L, Bartlow P, Gao Y, Gertsch J, Teramachi J, Kurihara N, Roodman GD, Cheng T, Xie XQ. “Lead Discovery, Chemistry Optimization and Biological Evaluation Studies of Novel Bi-amide Derivatives as CB2 Receptor Inverse Agonists and Osteoclast Inhibitors.” J Med Chem. 2012 Nov 26;55(22):9973-87. doi: 10.1021/jm301212u. Epub 2012 Oct 31. PMID: 23072339
  51. Kay M. Brummond, John Goodell, Matthew LaPorte, Lirong Wang and Xiang-Qun Xie. “Synthesis and In Silico Screening of a Library of Carboline-Containing Compounds”.    Beilstein J. Org. Chem. 2012, 8, 1048–1058. PMID:23019432
  52. Kyaw-Zeyar Myint, Lirong Wang, Qin Tong and Xiang-Qun Xie. “Fingerprint-based Artificial Neural Networks QSAR (FANN-QSAR) for Ligand Biological Activity Predictions”.  Mol. Pharm. 2012, 9(10):2912-23. PMID: 22937990
  53. Ajay Srinivasan, Lirong Wang, Cari J. Cline,Zhaojun Xie,Robert W. Sobol, Xiang-Qun Xie and Barry Gold. “Identification and characterization of human apurinic/apyrimidinic endonuclease-1 inhibitors”. Biochemistry, 2012, 51 (31), pp 6246–6259. PMID: 22788932
  54. Peng Yang, Lirong Wang and Xiang-Qun Xie. Latest advances in novel cannabinoid CB2 ligands for drug abuse and their therapeutic potential. Future 2012, 4(2):187-204.
  55. Lirong Wang*, Chao Ma* and Xiang-Qun Xie. “Linear and Non-linear Support Vector Machine for the Classification of Human 5-HT1A Ligand Functionality”, Molecular Informatics, 2012. 31(1): p. 85-95. (*these authors contributed equally)
  56. Yuxun Zhang, Zhaojun Xie, Lirong Wang, Brielle Schreiter, John S Lazo, Jurg Gertsch, Xiang-Qun Xie. “Mutagenesis and computer modeling studies of a GPCR conserved residue W5.43(194) in ligand recognition and signal transduction for CB2 receptor”, Int Immunopharmacol. 11( 9), Sep. 2011, 1303–1310
  57. Kyaw Myint, Chao Ma, Lirong Wang and Xiang-Qun Xie. “Fragment-based QSAR Algorithm Development for Compound Bioactivity Prediction”, SAR QSAR Environ Res. 2011 Jun;22(3):385-410
  58. Chao Ma, Lirong Wang and Xiang-Qun Xie. “GPU Accelerated Chemical Similarity Calculation for Compound Library Comparison”,  J. Chem. Inf. Model., Publication Date (Web): June 21, 2011
  59. Thomas O. Painter, Lirong Wang, Supriyo Majumder, Xiang-Qun Xie and Kay M. Brummond. “Diverging DOS Strategy Using an Allene-Containing Tryptophan Scaffold and a Library Design that Maximizes Biologically Relevant Chemical Space While Minimizing the Number of Compounds”,  ACS Comb Sci. 2011 Mar 14;13(2):166-74

  60. Chao Ma, Lirong Wang and Xiang-Qun Xie. “Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS) and Its Application on Modeling Ligand Functionality for 5HT-Subtype GPCR Families”,  J Chem Inf Model. 2011 Mar 28;51(3):521-31.
  61. Lirong Wang, Zhaojun Xie, Peter Wipf and Xiang-Qun Xie. “Residue Preference Mapping of Ligand Fragments in PDB”,  J Chem Inf Model. 25;51(4):807-15. Epub 2011 Mar 18.
  62. Wenlong Xu, Minghui Wang, Xianghua Zhang, Lirong Wang and Huanqing Feng. “SDED: A novel filter method for cancer-related gene selection”, Bioinformation 2008, 2, (7), 301-3.
  63. Lirong Wang, Zhaohui Jiang, Wenlong Xu and Huanqing Feng. “Analysis of Abnormal Transcription Factors and Pathways from Gastric Cancer Chips”, Journal of University of Science and Technology of China 2007, 12, 1539-04(in Chinese)
  64. Minghui Wang, Lirong Wang, Wenlong Xu, Xiaojun Lin, Zhaohui Jiang and Huanqing Feng “Phosphorylation Site Prediction Based on K-Nearest Neighbor Algorithm and BLOSUM62 Matrix ”, Chinese Journal of Biomedical Engineering 2007, 26, 404-408 (in Chinese)
  65. Yu Xue, Ao Li, Lirong Wang, Huanqing Feng and Xuebiao Yao. “PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory”, BMC Bioinformatics 2006, 7, 163.
  66. Lirong Wang and Huanqing Feng. “Mining Biomedical Literature Based on Bayesian Statistics”, Chinese Journal of Biomedical Engineering 2006, 25, 438-441(in Chinese)


  1. Title: Accelerating Treatment Development for Psychosis in AD: MODEL-AD+P
    Supporting Agency: NIH/National Institute of Mental Health, 2R01MH116046
    Performance Period: 09/25/2018-06/30/2028
    Project Goals: Individuals who develop psychotic symptoms such as delusions or hallucinations during Alzheimer disease have a more rapid deterioration and worse outcomes. In this grant we will evaluate whether compensations in the proteins present in brain synapses confer resilience to psychosis onset during Alzheimer disease, and whether genetic factors associated with resilience to psychosis in Alzheimer disease induce similar, protective compensations in an animal model. Finally, we will use the profile of protein compensation to identify novel medications that may treat and/or prevent psychosis in Alzheimer disease.
    My role in this project is mPI (Sweet, Kofler, Wang)
  2. Title: Leveraging Real-World Evidence from Electronic Medical Records to Explore the Multifactorial Pathophysiology of Heart Failure (HF)
    Supporting Agency: Eli Lilly and Company 46285
    Contracting/Grants Officer:
    Performance period: 03/02/2021-03/31/2024
    Project Goals: The goal of the study is to integrate high quality echo and hemodynamic data of HF patients through data-mining the original (raw) medical notes and create preliminary hypothesis through statistical analysis on outcomes for next stage research.
    My role in this project is PI
  3. Title: Leverage Electronic Medical Records to Identify Medications Repurposing for Treatment of ASUD with Comorbid PTSD
    Supporting Agency: RTI/subcontract of DOD# W81XWH-15-2-0077 (Dr. Nolen at RTI)
    Contracting/Grants Officer:
    Performance period: 11/01/2022-10/31/2024
    Project Goals: The objective of the proposed research is to identify medications with the potential for PTSD+ASUD treatment by analyzing the electronic medical records (EMR) of these patients.
    My role in this project is subcontract PI

Previous (Past 5 years)

  1. Title: NIDA Center of Excellence OF Computational Drug Abuse Research (CDAR)
    Supporting Agency: NIH/NIDA P30 PDA035778
    Contracting/Grants Officer: Paul Hillery
    Performance Period: 07/01/2014-06/30/20
    Project Goal: The overall goal of the Computational Drug Abuse Research (CDAR) Center is to advance state- of-the art computational technologies for research toward the prevention and treatment of drug abuse (DA).
    My role in this project is co-I
  2. Title: Chemogenomics Systems Pharmacology Approach for TBI and AD Research
    Time Commitments: 1.8 calendar
    Supporting Agency: DOD W81XWH-16-1-049
    Performance Period: 09/01/2016-08/30/19
    Project Goal: The objective of the proposed research is to further construct a TBI chemogenomic database based on our previously constructed AD chemogenomic database (AlzPlatform) (cover story: JCIM 2014, 54:1050-60) and apply our established algorithms/technologies to identify the common/shared pathways and possible drug targets between these two diseases. We will then carry out chemical genomics (i.e. chemogenomics) systems pharmacology (CGSP) analysis to predict possible modulators on these targets. The predictions generated from CGSP analyses will serve as driving hypotheses for future experimental validation and will reveal new drug-target associations to guide further drug development for TBI treatment.
    My role in this project is co-I