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Showing 376 - 390 of 582 results
  • Kho A, Hayes M, Rasmussen-Torvik L, Pacheco J, Thompson WK, Armstrong L,...Lowe W [including Peissig PL, Miller AW.] (2012 March). Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION. 19(2):212-8.
    PubMed ID: 22101970
  • Horne BD, Lenzini P, Wadelius M, Jorgensen AL, Kimmel SE, Ridker PM,...Gage BF [including Burmester JK, Caldwell MD.] (2012 February). Pharmacogenetic warfarin dose refinements remain significantly influenced by genetic factors after one week of therapy. THROMBOSIS AND HAEMOSTASIS. 107(2):232-40.
    PubMed ID: 22186998
  • Liu J, Peissig PL, Zhang C, Burnside E, McCarty CA, Page D. (2012). High-Dimensional Structured Feature Screening Using Binary Markov Random Fields. JMLR Workshop Conf Proc. 22 :712-721.
    PubMed ID: 23606924
  • Weiss JC, Natarajan S, Peissig PL, McCarty CA, Page D. (2012). Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health records. Innovative Applications of Artificial Intelligence (IAAI). 2012
  • Liu J, Zhang C, McCarty CA, Peissig PL, Burnside E, Page D. (2012). Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI). 2012
  • Kawaler E, Cobian A, Peissig PL, Cross DS, Yale SH, Craven M. (2012). Learning to Predict Post-Hospitalization VTE Risk from EHR Data. AMIA Annu Symp Proc. 2012 :436-45.
    PubMed ID: 23304314
  • McCarty CA, Berg RL, Welter JD, Kitchner TE, Kemnitz JW. (2012 January). A novel gene-environment interaction involved in endometriosis. Int J Gynaecol Obstet. 116(1):61-3.
    PubMed ID: 22024213
  • Page D, Costa V, Natarajan S, Barnard A, Peissig PL, Caldwell MD. (2012). Identifying Adverse Drug Events by Relational Learning Association for the Advancement of Artificial Intelligence. :15999-1605.
  • Davis J, Costa V, Peissig PL, Caldwell MD, Berg E, Page D. (2012). Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events Proceedings of the 29th International Conference on Machine Learning.
  • Weiss JC, Page D, Peissig PL, Natarajan S, McCarty C. (2012). Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records. Proc Innov Appl Artif Intell Conf. 2012 :2341-2347.
    PubMed ID: 25360347
  • Pichereau S, Pantrangi M, Couet W, Badiou C, Lina G, Shukla SK, Rose WE. (2012 January). Simulated antibiotic exposures in an in vitro hollow-fiber infection model influence toxin gene expression and production in community-associated methicillin-resistant Staphylococcus aureus strain MW2. Antimicrob Agents Chemother. 56(1):140-7.
    PubMed ID: 22064533
  • Pichereau S, Moran JJ, Hayney MS, Shukla SK, Sakoulas G, Rose WE. (2012 January). Concentration-dependent effects of antimicrobials on Staphylococcus aureus toxin-mediated cytokine production from peripheral blood mononuclear cells. J Antimicrob Chemother. 67(1):123-9.
    PubMed ID: 21980070
  • John Jr JF, Shukla SK. (2012). Staphylococcus aureus. In: Mayhall CG (Ed.), Hospital Epidemiology and Infection Control. (4th ed.). (pp. 385-409). Philadelphia, PA: Lippincott Williams & Wilkins.
  • Foley SL, Nayak R, Shukla SK, Johnson TJ. (2012). Current Issues, Challenges and Future Directions for Subtyping of Bacterial Foodborne Pathogens. In: Foley SL, Nayak R, Johnson TJ, Shukla SK (Ed.), Molecular Typing Methods for Tracking Foodborne Microorganisms (pp. 381-90). New York, NY:: Nova Science Publishers, Inc.
  • Rose WE, Shukla SK. (2012). Antimicrobial Resistance in Foodborne Pathogens. In: Foley SL, Nayak R, Johnson TJ, Shukla SK (Ed.), Molecular Typing Methods for Tracking Foodborne Microorganisms (pp. 65-87). New York, NY:: Nova Science Publishers, Inc.