Xia Jiang, PhD

Leveraging Bayesian networks and information theory to learn risk factors for breast cancer metastasis.

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

Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inferenc

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.

Conjugated equine estrogen and medroxyprogesterone acetate are associated with decreased risk of breast cancer relative to bioidentical hormone therapy and controls.

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.

Using natural language processing and machine learning to identify breast cancer local recurrence

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.

Artifical intelligence: With an Introduction to Machine Learning, Second Edition

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

Modeling miRNA-mRNA interactions that cause phenotypic abnormality in breast cancer patients

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.

Defining and Discovering Interactive Causes

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.

An algorithm for direct causal learning of influences on patient outcomes.

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. 

An informatics research agenda to support precision medicine: seven key areas

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.

Discovering causal interactions using Bayesian network scoring and information gain

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.

^