Xia Jiang, PhD

Research Interests: 
  • Application of artificial intelligence, machine learning, Bayesian networks, and other computational methods to problems in biology, medicine, and translational research
  • Modeling of interactome networks and human diseases
  • Personalized medicine and cancer bioinformatics
  • Medical decision support systems
  • Biosurveillance system development
  • Image processing
Assistant Professor, Department of Biomedical Informatics
Biomedical Informatics Training Program Core Faculty
University of Pittsburgh School of Medicine
Selected Publications: 

Jiang X, Visweswaran S, Neapolitan RE. Mining Epistatic Interactions from High-Dimensional Data Sets Using Bayesian Networks. In: Holmes D, Jain L, editors. Foundations and Intelligent Paradigms-3. Berlin, Heidelberg: Springer-Verlag, 2011.

Neapolitan RE, Jiang X. A Note of Caution on Maximizing Entropy. Entropy. 2014; 16 (7):4004-14. doi: 10.3390/e16074004.

Neapolitan RE, Xue D, Jiang X. Modeling the Altered Expression Levels of Genes on Signaling Pathways in Tumors as Causal Bayesian Networks. Cancer Informatics. 2014; 13:77-84. PMCID: PMC4051800. doi: 10.4137/CIN.S13578.

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 B, Jiang X. Novel Artificial Neural Network Method for Biomedical Prediction based on Matrix Pseudo-Inversion. Journal of Biomedical Informatics. 2014 Apr; 48:114-21. doi: 10.1016/j.jbi.2013.12.009.

Jiang X, Xue D, Brufsky AM, Khan SA, Neapolitan RE. A new method for predicting patient survivorship using efficient Bayesian network learning. Cancer Informatics. 2014; 13 (2):47-57. PMCID: PMC3928477. PMID: 24558297[PubMed]. doi: 10.4137/CIN.S13053.

Neapolitan RE, Jiang X. Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data. Cancer Informatics Supplement on Cancer Clinical Information Systems. 2014; 13(s1): 29-36. doi: 10.4137/CIN.S13881

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

Neapolitan RE, Jiang X. Contemporary Artificial Intelligence. 1st ed. Boca Raton, FL: Chapman and Hall/CRC, 2012.

Jiang X, Neapolitan RE. Mining Strict Epistatic Interactions From High-Dimensional Datasets: Ameliorating the Curse of Dimensionality. PLoS ONE. 2012; 7 (10):e46771. PMCID: PMC3470561. PMID: 23071633

Jiang X, Barmada MM, Becich MJEvaluating de novo locus-disease discoveries in GWAS using the signal-to-noise ratio. AMIA Annu Symp Proc. 2011;2011:617-24. Epub 2011 Oct 22. PMID: 22195117 

Jiang, X., R.E. Neapolitan, M.M. Barmada, S.Visweswaran. Learning genetic epistasis using Bayesian network scoring criteria. BMC Bioinformatics; 2011: 12(89). PMCID: PMC3080825

Jiang X, Neapolitan RE, Barmada M, Visweswaran S, Cooper GF.   A fast algorithm for learning epistatic genomic relationships.  In: Proceedings of the Annual Symposium of the American Medical Informatics Association (2010) 341-345. PMID: 21346997 PMC3041370

Jiang, X., M. M. Barmada, and S. Visweswaran, “Identifying Genetic Interactions in Genome-Wide Data Using Bayesian Networks”, Vol. 34, No. 6 (September, 2010), p 575-81, Genetic Epidemiology.PMID: 20568290 [PubMed in process]

Chakrabarti, S., Jiang X., et al. Data Mining: Know It All, Morgan Kaufmann, Burlington, MA, 2009

Jiang X, Cooper GF. Modeling the Temporal Trend of the Daily Severity of an Outbreak using Bayesian Networks. In: Holmes DE, Jain LC, editors. Innovations in Bayesian Networks of Studies in Computational Intelligence. NY: Springer-Verlag, 2008.

Neapolitan RE, Jiang X. Probabilistic Methods for Financial and Marketing Informatics. San Mateo, CA: Morgan Kaufmann, 2007.

Neapolitan RE, Jiang X. A Tutorial on Learning Casual Influences. In: Holmes DE, Jain LC, editors. Innovations in Machine Learning. NY: Springer-Verlag, 2006