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
Associate Professor, Department of Biomedical Informatics
Biomedical Informatics Training Program Core Faculty
University of Pittsburgh School of Medicine
Publications: 

Zeng Z, Jiang X, Li X, Wells A, Luo Y, Neapolitan RE. Conjugated equine estrogen and medroxyprogesterone acetate are associated with decreased risk of breast cancer relative to bioidentical hormone therapy and controls. PLoS One. 2018 May 16;13(5):e0197064. doi: 10.1371/journal.pone.0197064. eCollection 2018. PMID: 29768475.

Lee S, Jiang X. Modeling miRNA-mRNA interactions that cause phenotypic abnormality in breast cancer patients. PLOS One. Online 9 August 2017.

Rathnam C, Lee S, Jiang X. An algorithm for direct causal learning of influences on patient outcomes. Artif Intell Med. 2017 Jan; 75: 1-15. doi: 10.1016/j.artmed.2016.10.003. Epub 2016 Nov 5. PMID: 28363452. PMC ID: PMC5415921

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. 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, 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.

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.

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