Jason Xuan promoted to Professor (Jun. 2015)
ECE team awarded NIH grant for breast cancer research (2013 Annual Report)
Jason Xuan receives tenure (June 2012)
Mathematical modeling to fight breast cancer (2012 Annual Report)
Xuan awarded $1.56 million cancer research grant (March 2011)
Reverse engineering a network to fight breast cancer (2011 Annual Report)
Battling cancer: Understanding signaling networks (April 2009)
Unraveling the environmental and genetic factors of common diseases (2008 Annual Report)
Office / Address: Virginia Tech Research Center - Arlington, Room 4-030
Arlington, VA 22203
Tel: (571) 858-3151
Fax: (571) 858-3015
vt.edu Email: xuan
Affiliated Research Group
Computational Bioinformatics and Bioimaging Laboratory
Ph.D., Electrical Engineering & Computer Science, University of Maryland, 1997
Ph.D. Electrical Engineering, University of Zhejiang, 1991
M.S., Electrical Engineering, University of Zhejiang, 1988
B.S., Electrical Engineering, University of Zhejiang, 1985
Visual intelligence & computer vision, pattern recognition, bioinformatics, computational systems biology, machine learning, digital image processing, information visualization, computer graphics, multimedia processing, and information retrieval.
Computational systems biology, bioinformatics for cancer research, intelligent computing, information visualization, advanced image analysis, cellular and molecular imaging, and image guided radiation therapy.
- Uncovering Estrogen Receptor-Signaling Networks to Overcome Endocrine Resistance (NIH/NCI R01CA149653; PI)(http://www.cbil.ece.vt.edu/ResearchOngoingER.htm)
- Transgenerational Effects of Maternal High Fat Diet During Pregnancy of Breast Cancer (NIH/NCI R01CA164384; Co-PI) (http://www.cbil.ece.vt.edu/ResearchOngoingEpiG.htm)
- Integration of ER-related Signaling in Breast Cancer (NIH/NCI U54CA149147; Co-I)
- Improved Diagnostics of the Muscular Dystrophies (NIH/NINDS R01NS29525-18A1; Co-I)
- Genomic and Proteomic Architecture of Atherosclerosis (NIH/NHLBI R01HL111362; Co-I)
- L. Chen, J. Xuan*, R. B. Riggins, Y. Wang and R. Clarke, “Identifying protein interaction subnetworks by a bagging Markov random field-based method,” Nucleic Acids Res. (Impact Factor (IF) = 8.808), 41(2):e42, 2013.
- J. Gu, J. Xuan*, R. B. Riggins, L. Chen, Y. Wang, and R. Clarke, “Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic,” Bioinformatics (IF=5.323), pp. 1990-1997, 2012.
- X. Chen, J. Xuan*, C. Wang, A. N. Shajahan, R. B. Riggins, and R. Clarke, “Reconstruction of transcriptional regulatory networks by stability-based network component analysis,” IEEE/ACM Trans. Computational Biology and Bioinformatics (IF=1.536), pp. 1347-58, November 2013.
- X. Chen, M. M. Thiaville, L. Chen, A. Stoeck, J. Xuan*, M. Gao, I.-M. Shih, and T.-L. Wang, “Definition of NOTCH3 target genes in ovarian cancer,” Cancer Research (IF = 8.650), pp. 2294-2303, 2012.
- L. Chen, J. Xuan*, R. B. Riggins, R. Clarke, and Y. Wang, “Identifying cancer biomarkers by network-constrained support vector machines,” BMC Systems Biology (IF = 3.15), 2011.
- T. Gong, J. Xuan*, L. Chen, R. B. Riggins, H. Li, E. P. Hoffman, R. Clarke, and Y. Wang, “Motif-guided sparse decomposition of gene expression data for regulatory module identification,” BMC Bioinformatics (IF = 2.75), 2011.
- L. Chen, J. Xuan*, R. B. Riggins, Y. Wang, E. P. Hoffman, and R. Clarke, “Multilevel support vector regression analysis to identify condition-specific regulatory networks,” Bioinformatics (Impact Factor (IF) = 4.877), vol. 26, no. 11, pp. 1416-1422, 2010.
- C. Wang, J. Xuan*, H. Li, Y. Wang, M. Zhan, E. P. Hoffman and R. Clarke, “Knowledge-guided gene ranking by coordinative component analysis,” BMC Bioinformatics (IF = 3.03), vol. 11, 2010.
- Y. Zhang, J. Xuan, B. G. de los Reyes, R. Clarke, and H. W. Ressom, “Reverse engineering module networks by PSO-RNN hybrid modeling,” BMC Genomics (IF = 3.759), 2009.
- J. Xuan*, Y. Wang, Y. Dong, Y. Feng, B. Wang, J. Khan, M. Bakay, Z. Wang, L. Pachman, S. Winokur, Y.-W. Chen, R. Clarke, and E. Hoffman, “Gene selection for multiclass prediction by weighted Fisher criterion,” EURASIP Journal of Bioinformatics and Systems Biology, vol. 2007, 2007.