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Xinghua Lu
Xinghua Lu Associate Professor


Institution of Highest Degree:
University of Connecticut Health Center

Dr. Lu’s research focuses on the computational methods for identifying signaling pathways underlying biological processes and diseases as well as statistical methods for acquiring knowledge from biomedical literature. He was trained in Pharmacology and works in the field of bioinformatics after NLM sponsored postdoctoral training in Biomedical Informatics. His research interest concentrates on applying latent variable models to simulate biological signaling system and text mining.

Currently, Dr. Lu is working on developing his research in translational bioinformatics and systems/computational biology and its application to specific domains relevant to human disease. He is pursuing collaboration in the area of natural language processing and text mining with the eventual goal of establishing a Center or Institute in Translational Bioinformatics.

Research Interests

Computational methods for identifying signaling pathways underlying biological process and diseases
Statistical methods for acquiring knowledge from biomedical literature
Translational bioinformatics and systems/computational biology
Natural language processing and text mining

All Publications

View in D-Scholarship

Recent Publications

Vicky Chen, John Paisley and Xinghua Lu. Revealing common disease mechanisms shared by tumors of different tissues of origin through semantic representation of genomic alterations and topic modeling. BMC Genomics 2017 18(Suppl2):105. DOI: 10.1186/s12864-017-3494-z

Cooper GF, Bahar I, Becich MJ, Benos PV, Berg J, Espino JU, Jacobson RC, Kienholz M, Lee AV, Lu X, Scheines R, Center for Causal Discovery team. The Center for causal discovery of biomedical knowledge from Big Data. Journal of the American Medical Informatics Association 2015 Jul 2. pii: ocv059. doi: 10.1093/jamia/ocv059. [Epub ahead of print]  PMID: 26138794

Ogoe, HA, Visweswaran, S, Lu, X, Gopalakrishnan, V.  (2015) Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression data.  BMC Bioinformatics 16:226 (designated as a Highly Accessed paper) PMID: 26202217 PMCID: PMC4512094

Jiang X, Cai B, Xue D, Lu X, Cooper GF, Neapolitan RE. A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets. Journal of the American Medical Informatics Association (2014). Oct;21(e2):e312-9. doi: 10.1136/amiajnl-2013-002358. Epub 2014 Apr 15.  PMID: 24737607 PMC4173174

Cai C, Chen L, Jiang X, Lu X. Integrating protein phosphorylation and gene expression data to infer signaling pathways. Cancer Informatics Supplement on Cancer Clinical Information Systems. 2014; 13(s1): 59-67. doi: 10.4137/CIN.S13883