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James A. Foster

Affiliations: 
University of Idaho, Moscow, ID, United States 
Area:
Computer Science
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"James Foster"
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Parents

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Children

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Bart I. Rylander grad student 2001 University of Idaho
Kosuke Imamura grad student 2002 University of Idaho
Mark M. Meysenburg grad student 2002 University of Idaho
Conrad Shyu grad student 2006 University of Idaho
Lucas J. Sheneman grad student 2008 University of Idaho
Daniel Beck grad student 2014 University of Idaho

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BETA: Related publications

Publications

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Lackey KA, Williams JE, Meehan CL, et al. (2019) What's Normal? Microbiomes in Human Milk and Infant Feces Are Related to Each Other but Vary Geographically: The INSPIRE Study. Frontiers in Nutrition. 6: 45
Beck D, Foster JA. (2015) Machine learning classifiers provide insight into the relationship between microbial communities and bacterial vaginosis. Biodata Mining. 8: 23
Zhbannikov IY, Foster JA. (2015) MetAmp: combining amplicon data from multiple markers for OTU analysis. Bioinformatics (Oxford, England). 31: 1830-2
Beck D, Dennis C, Foster JA. (2015) Seed: a user-friendly tool for exploring and visualizing microbial community data. Bioinformatics (Oxford, England). 31: 602-3
Beck D, Foster JA. (2015) Machine learning classifiers provide insight into the relationship between microbial communities and bacterial vaginosis Biodata Mining. 8: 1-9
Baker YS, Agrawal R, Foster JA, et al. (2014) Detecting Bacterial Vaginosis Using Machine Learning. Proceedings of the 2014 Acm Southeast Regional Conference / Association For Computing Machinery-Digital Library. 2014
Baker YS, Agrawal R, Foster JA, et al. (2014) APPLYING MACHINE LEARNING TECHNIQUES IN DETECTING BACTERIAL VAGINOSIS. Proceedings / International Conference On Machine Learning and Cybernetics. International Conference On Machine Learning and Cybernetics. 2014: 241-246
Carter J, Beck D, Williams H, et al. (2014) GA-Based Selection of Vaginal Microbiome Features Associated with Bacterial Vaginosis. Genetic and Evolutionary Computation Conference : [Proceedings] / Sponsored by Acm Sigevo. Genetic and Evolutionary Computation Conference. 2014: 265-268
Beck D, Foster JA. (2014) Machine learning techniques accurately classify microbial communities by bacterial vaginosis characteristics. Plos One. 9: e87830
Baker YS, Beck D, Agrawal R, et al. (2014) Detecting Bacterial Vaginosis using machine learning Proceedings of the 2014 Acm Southeast Regional Conference, Acm SE 2014
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