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.
Associate Professor of Biomedical Informatics
Biomedical Informatics Training Program Core Faculty
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
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
David J. Montefusco, Lujia Chen, Nabil Matmati, Songjian Lu, Benjamin Newcomb, Gregory F. Cooper, Yusuf A. Hannun, and Xinghua Lu. Science Signaling, 6(299): p. rs14 [DOI: 10.1126/scisignal.2004515] http://stke.sciencemag.org/cgi/content/full/sigtrans;6/299/rs14
Zhao, Z., Shen, B., Lu, X., and Vongsangnak, W (2013) Translational Biomedical Informatics and Computational Systems Medicine. BioMed Research International, vol. 2013, Article ID 237465, 2013. doi:10.1155/2013/237465
Montefusco, D., Chen, L, Matmati, N., Lu, S., Newcomb, B., Cooper, GF., Hannun, YA., Lu, X., (2013) Distinct signaling roles of ceramide species in yeast revealed through systematic perturbation and integromics analyses. Science Signaling 6:rs14
Osmanbeyoglu, H, Lu, K., Oesterreich, S, Day, RS, Benos, PV, Coronnello, C., and, Lu, X (2013) Estrogen represses gene expression through chromatin reconfiguration. Nucleic Acid Research 41(17): 8061-8071
Chen, V and Lu, X (2013) Conceptualization of molecular findings by mining gene annotations. BMC Proceedings 7(Suppl 7):S2
Lu, S., Jin, B., Cowart, LA., and Lu, X (2013) From data towards knowledge: Revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data. PLoS One 8(4): e61134
Mowrey, D., Cheng, M., Liu, L., Willenbring, D., Lu, X., Wymore, T., Xu, Y., and Tang, P.. (2013) Asymmetric ligand binding facilitates conformational transitions in pentameric ligand-gated ion channels. J Am Chem Soc. 135(6):2172-80. PMCID: PMC3582375
Lu, S., and Lu, X (2013) Using graph model to find transcription factor modules: the hitting set problem and an exact algorithm. Algorithms for Molecular Biology 8:2 PMCID: PMC3622577
Lu, S. and Lu, X (2012) Integrating genome and functional genomics data to reveal perturbed signaling pathways in ovarian cancers. Proceedings of AMIA Summit on Translational Bioinformatics, San Francisco.
Qin, T., Tsoi, LC., Sims KJ, Lu, X and Zheng, WJ (2012) Signaling network prediction using the ontology fingerprint enhanced Bayesian networks. BMC Systems Biology 6 (Suppl 3) : S3 (co-corresponding author)
Richards, A., Schwacke, J., Rohrer, B., Cowart, LA. and Lu, X (2012) Revealing functionally coherent gene subset using spectral clustering and information integration approaches. BMC Systems Biology 6 (Suppl 3) : S7
Osmanbeyoglu, H, Hartmaier, R., Oesterreich, S., and Lu, X. (2012) Improving ChIP-seq peak-calling for functional indirect co-regulator binding by integrating multiple sources of biological information. BMC Genomics 13(Suppl 1):S1
Feng, H, Hu, B, Liu, KW, Lu, X, Yiin, JJ, Lu, S, Keezer, S, Fenton, T, Furnari, FB, Hamilton, RL, Vuori, K, Nagane, M, Nishikawa, R, Cavenee, WK and Cheng, SY (2012) Aberrant Activation of Rac1 by Src-dependent Phosphorylation of Dock180Y1811 Mediates PDGFRα-stimulated Glioma Growth and Invasion. Journal of Clinical Investigation 121(12):4670–4684
Lu, S and Lu, X (2011). A graph model and an exact algorithm for finding cooperative transcription factor modules. Proceedings of ACM Bioinformatics and Computational Biology 2011
Jin, B., Chen, V., Chen, L., and Lu, X. (2011) Mapping annotations with textual evidence using an scLDA model. Proceedings of AMIA Annual Symposium, Washington DC
Jin, B. and Lu, X (2010). Identifying informative subsets of the Gene Ontology with information bottleneck methods. Bioinformatics 26 (19): 2445-2451
Cowart, LA., Shotwell, M., Worley, ML., Richards, AJ, Montefusco, DJ, Hannun YA, and Lu, X. (2010) Revealing a signaling role of PHS1P in yeast using integrative systems approaches. Molecular Systems Biology 6:349 PMID: 20160710(Selected for presentation at the Highlight Track of ISMB 2010 as a major advance in the field)
Richards*, AJ, Muller, B., Shotwell, M, Cowart, LA, Rohrer, B, and Lu, X (2010) Assessing the functional coherence of gene sets with metrics based on the Gene Ontology graph. Bioinformatics, supplement issue for the Proceedings for the Intelligent Systems in Molecular Biology (ISMB) 2010 (19% acceptance) PMID: 20529941
Zheng*, B. and Lu, X (2009) Application of semantic modeling in bioinformatics domain. In: Data Management in Semantic Web, Ed. Jin, H. and Lv, Z,. Nova Science Publishers, Inc.
Jin*, B and Lu, X (2009) Enhancing GO-graph-based multi-label classification using semantic-rich GO terms. Proceedings of the Annual Meeting of the ISMB BioLINK Workshop 2009
Muller*, B., Richards, AJ., Jin, B., Lu, X. (2009) GOGrapher; A Python library for GO graph representation and analysis. BMC Research Notes 2:122 PMID:19583843