Publications for Xia Jiang, PhD

2020

Jiang X, Wells A, Drufsky A, Shetty D, Sajihan K, Neapolitan RE.  Leveraging Bayesian networks and information theory to learn risk factors for breast cancer metastasis.  BMC Bioinformatics 2020 Jul 10;21(1):298. doi: 10.1186/s12859-020-03638-8. PMID: 32650714 PMCID: PMC7350636

2019

Cai C, Cooper GF, Lu KN, Ma X, Xu S, Zhao Z, Chen X, Xue Y, Lee AV, Clark N, Chen V, Lu S, Chen L, Yu L, Hochheiser HS, Jiang X, Wang JQ, Lu X. Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference. PLOS Comput Biol, 2019 Jul 15(7):e1007088. doi: 10.1371/journal.pcbi.1007088. [Epub ahead of print] PMID: 31276486; PMCID: PMC6650088.

2018

Zeng Z, Espino S, Roy A, Li X, Khan SA, Clare SE, Jiang X, Neapolitan R, Luo Y. Using natural language processing and machine learning to identify breast cancer local recurrence. BMC Bioinformatics. 2018 Dec 28:19(Suppl 17):498. doi: 10.1186/s12859-018-2466-x. PMID: 30591037. PMCID: PMC6309052.

Neapolitan RE, Jiang X. Artifical intelligence: With an Introduction to Machine Learning, Second Edition. Chapman & Hall/CRC Artifical Intelligence and Robotics Series.Textbook. 2018.

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. PMCID: PMC5955567.

2017

Neapolitan RE, Jiang X. Defining and Discovering Interactive Causes. In Holmes D and Jain L, editors. Advances in Biomedical Informatics, pp 53-78. Springer, October 2017.

Lee S, Jiang X. Modeling miRNA-mRNA interactions that cause phenotypic abnormality in breast cancer patients. PLOS One.2017 Aug 9;12(8):e1082666. doi: 10.1371/journal.pone.0182666. eCollection 2017. PMID: 28793339. PMCID: PMC5549916.

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. 

2016

Zeng Z, Jiang X, Neapolitan R. Discovering causal interactions using Bayesian network scoring and information gain. BMC Bioinformatics. 2016 May 26;17(1):221. doi: 10.1186/s12859-016-1084-8. PMID: 27230078. PMCID: PMC4880828.

Hill SM, Heiser LM, Cokelaer T, Jiang X, et. al. Inferring causal molecular networks: empirical assessment through a community-bsed effort. Nat Methods. 2016 Apr;13(4):310-8. doi: 10.1038/nmeth.3773. Epub 2016 Feb 22. PMID: 26901648. PMCID: PMC4854847.

Tenenbaum JD, Avillach P, Benhan-Hutchins M, Breitenstein MK, Crowgey EL, Hoffman MA, Jiang X, Madhavan S, Mattison JE, Nagarajan R, Ray B, Shin D, Visweswaran S, Zhao Z, Freimuth RR. An informatics research agenda to support precision medicine: seven key areas. J Am Med Inform Assoc. 2016 Jul;23(4):791-5. doi: 10.1093/jamia/ocv213. Epub 2016 Apr 23. PMID: 27107452. PMCID: PMC4926738.

Cai B, Jiang X. Computational methods for ubiquination site prediction using physicochemical properties of protein sequences. BMC Bioinformatics. 2016 Mar 3;17:116. doi: 10.1186/s12859-016-0959-z. PMID: 26940649. PMCID: PMC4778322.

Neapolitan RE, Jiang X. The Bayesian Network Story. In Hajek A and Hitchcock C (Eds.): The Oxford Handbook of Probability and Philosophy. Oxford University Press, 2016.

2015

Neapolitan R, Horvath CM, Jiang X. Pan-cancer analysis of TCGA data reveals notable signaling pathways. BMC Cancer. 2015 Jul 14;15:516. doi: 10.1186/s12885-015-1484-6. PMID: 26169172. PMCID: PMC4501083.

Jiang X, Neapolitan RE. Evaluation of a two-stage framework for prediction using big genomic data. Brief Bioinform. 2015 Nov;16(6):912-21. doi: 10.1093/bib/bbv010. Epub 2015 Mar 18. PMID: 25788325. PMCID: PMC4652616.

Neapolitan RE, Jiang X. Study of integrated heterogeneous data reveals prognostic power of gene expression for breast cancer survival. PLoS ONE. 2015 Feb 27;10(2):e0117658. DOI: 10.1371/journal.pone.0117658. PMID: 25723490. PMCID: PMC4344205.

Jiang X, Neapolitan RE. LEAP: biomarker inference through learning and evaluating association patterns. Genet Epidemiol. 2015 Mar;39(3):173-184. doi: 10.1002/gepi.21889. PMID: 25677188. PMCID: PMC4366363.

Jiang X, Jao J, Neapolitan R. Learning predictive interactions using information gain and Bayesian network scoring. PLoS ONE. 2015 Dec 1;10(12): e0143247. doi:10.1371/journal.pone.0143247. PMID: 26624895. PMCID: PMC4666609.

2014

Cai C, Chen L, Jiang X, Lu X. Modeling Signal Transduction from Protein Phosphorylation to Gene Expression. Cancer Inform. 2014 Oct 13; 13(Suppl 1): 59-67. doi: 10.4137/CIN.S13883. eCollection 2014. PMID: 25392684. PMCID: PMC4216050.

Jiang X, Xue D, Brufsky A, Khan S, Neapolitan R. A new method for predicting patient survivorship using efficient Bayesian network learning. Cancer Inform. 2014 Feb 13; 13 (2):47-57. doi: 10.4137/CIN.S13053. eCollection 2014. PMID: 24558297. PMCID: PMC3928477. 

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

Cai B, Jiang X. A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion. J Biomed Inf. 2014 Apr;48:114-21. doi: 10.1016/j.jbi.2013.12.009. Epub 2013 Dec 18. PMID: 24361387. PMCID: PMC4004678.

Jiang X, Chen R, Cheng S, Shen B, Xu R, Yi S. Computational advances in cancer informatics (a). Cancer Inform. 2014 Oct 13;13(Suppl 1):45-8. doi: 10.4137/CIN.S19243. eCollection 2014. PMID: 2548472. PMCID: PMC4216040.

Neapolitan RE, Jiang X. The Bayesian Network Story. In: Hajek A, Hitchcock C, editors. The Oxford Handbook of Probability and Philosophy. Oxford University Press, 2014.

Jiang X, Visweswaran S, Neapolitan RE. Scoring and Searching Bayesian Network Models of Gene-Phenotype Association. In: Sinoquet C, Mourad R, editors. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics. Oxford University Press, 2014.

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. J Am Med Inform Assoc. 2014 Oct;21(e2):e312-9. doi: 10.1136/amiajnl-2013-002358. Epub 2014 Apr 15. PMID: 24737607. PMCID: PMC4173174.

Neapolitan RE, Xue D, Jiang X. Modeling the Altered Expression Levels of Genes on Signaling Pathways in Tumors as Causal Bayesian Networks. Cancer Inform. 2014 May 15; 13:77-84. doi: 10.4137/CIN.S13578. PMID: 24932098. PMCID: PMC4051800.

Cai B, Jiang X. Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data using Gene Set Enrichment Analysis. Cancer Inform. 2014 Dec 1;13(Suppl 1):113-21. doi: 10.4137/CIN.S13882. eCollection 2014. PMID: 25520551. PMCID: PMC4251186.

Neapolitan R, Jiang X. Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data. Cancer Inform. 2014 Oct 13;13(Suppl 1): 29-36. doi: 10.4137/CIN.S13881. eCollection 2014. PMID: 25392681. PMCID: PMC4216062.

2012

Jiang X, Neapolitan RE. Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionality. PLoS ONE. 2012;7(10):e46771.doi: 10.1371/journal.pone.0046771. Epub 2012 Oct 12. PMID: 23071633. PMCID: PMC3470561.

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

Sverchkov Y, Jiang X, Cooper GF.  Spatial cluster detection using dynamic programming. BMC Medical Informatics & Decision Making 12:22(2012).  Doi:10.1186/1472-6947-12-22.  PMID: 22443103. PMC: PMC3403878.

2011

Jiang X, Barmada MM, Cooper GF, Becich MJ. A Bayesian method for evaluating and discovering disease loci associations. PLoS ONE. 2011;6(8):e22075. doi: 101371/journal.pone.0022075. Epub 2011 Aug 10. PMID: 21853025. PMCID: PMC3154195.

Jiang X, Visweswaran S, Neapolitan RE. Mining Epistatic Interactions from High-Dimensional Data Sets Using Bayesian Networks, in Holmes, D. and L. Jain (Eds.): Foundations and Intelligent Paradigms--3, Springer-Verlag, Berlin Heidelberg, 2011, DOI:10.1007/978-3-642-23151-3_9.

Jiang X, Neapolitan RE, Barmada MM, Visweswaran S. Learning genetic epistasis using Bayesian network scoring criteria. BMC Bioinformatics 2011 Mar 31;12:89. doi: 10.1186/1471-2105-12-89. PMID: 21453508. PMCID: PMC3080825.

Jiang X, Barmada MM, Becich MJ. Evaluating 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. PMCID: PMC3243170.

2010

Jiang X, Cooper G. Joint SNP analysis using a breast cancer GWAS data set. In: Proceedings of Cancer Informatics Workshop. Cancer Informatics Workshop; 2010; Cambridge UK 2010.

Jiang X, Neill DB, Cooper GF.  A Bayesian network model for spatial event surveillance. International Journal of Approximate Reasoning (2010) 51:224-239.  doi: ttp://dx.doi.org/10.1016/j.ijar.2009.01.001.

Jiang X, Cooper GF.  A real-time temporal Bayesian architecture for event surveillance and its application to patient-specific multiple disease outbreak detection. Data Mining and Knowledge Discovery (2010). DOI: 10.1007/s10618-009-0151-4.  

Jiang X, Cooper GF. A Bayesian spatio-temporal method for disease outbreak detection. Journal of the American Medical Informatics Association 17 (2010) 462-471. PMID: 20595315.  PMC2995651.

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, Barmada MM, Visweswaran S. Identifying genetic Interactions in genome-wide data using Bayesian networks. Genet Epidemiol. 2010 Sep;34(6):575-81. doi: 10.1002/gepi.20514. PMID: 20568290. PMCID: PMC3931553.

Jiang X, Neill DB, Cooper GF. On the robustness of Bayesian network based spatial event surveillance. International Journal of Approximate Reasoning, 51 (2010) p 224-239.  http://dx.doi.org/10.1016/j.ijar.2009.01.001.

2009

Neill DB, Cooper GF, Das K, Jiang X, Schneider J. Bayesian Networks Scan Statistics for Multivariate Pattern Detection. In: Glaz J, Pozdnyakov V, Wallenstein S, editors. Scan Statistics - Methods and Applications. Birkhauser, 2009.

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

Jiang X, Wallstrom G, Cooper GF, Wagner MM. Bayesian prediction of an epidemic curve. J Biomed Inform. 2009 Feb; 42(1):90-9. doi: 10.1016/j.jbi.2008.05.013. Epub 2008 Jun 13.  PMID: 18593605.  

Jiang X, Neill DB, Cooper GF.  Generalized AMOC curves for evaluation and improvement of event surveillance. In: Proceedings of the Annual Symposium of the American Medical Informatics Association (2009) 281-285. PMID: 20351865. PMC2815453.

2008

Jiang X, Cooper GF. A temporal method for outbreak detection using a Bayesian networks. In: Proceedings of the International Symposium on Disease Surveillance. 5(2) 105, 2008.

Jiang X. A Bayesian Network Model for Spatio-temporal Event Surveillance. University of Pittsburgh (PhD Thesis), 2008.

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.

2007

Jiang X. A Bayesian Network for Estimating and Predicting Epicurves. Advances in Disease Surveillance. 2007; 2:15.

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

Jiang X, Cooper GF. A recursive algorithm for spatial cluster detection. In: Proceedings of the Fall Symposium of the American Medical Informatics Association (2007) 369-373. PMID:  18693860. PMC2655859.

2006

Jiang X, Wallstrom GL. A Bayesian Network for Outbreak Detection and Prediction. 21st Association for the Advancement of Artificial Intelligence (AAAI) Conference. 2006:1155-60.

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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