James A. Foster, AB, MS, PhD

Affiliate Professor, Biomedical Informatics and Medical Education

University Distinguished Professor, University of Idaho

Adjunct Professor of Philosophy, University of Idaho

Adjunct Professor of Computer Science, University of Idaho



Bioinformatics algorithms for analysis of microbial ecosystems, microbiomes, evolutionary computation, genetic programming, agent based simulations, ethics, classical philosophy


A.B. in Philosophy from University of Chicago, M.S. and Ph.D. in Computer Science from Illinois Institute of Technology


Bioinformatics algorithm development, human milk microbiome, TTX production and the rough skinned newt skin microbiome, microbiome of sediment on the mid Atlantic ridge, emergence of signaling systems.


Graduate ethics, computing skills for biologists

Other Roles:

Site director for NSF BEACON Science and Technology Center, (former) Idaho INBRE Bioinformatics Director, science advisor to the University of Idaho IBEST Computational Resources Core

Representative publications:

  1. W Banzhaf, B Baumgaertner, G Beslon, R Doursat, JA Foster, B McMullin, VV de Melo, T Miconi, L Spector, S Stepney, R White (2016) Defining and Simulating Open-Ended Novelty: Requirements, Guidelines, and Challenges. Theory in Biosciences, In Press
  2. CS Greene, JA Foster, BA Stanton, DA Hogan, Y Bromberg (2016) Computational approaches to study microbes and microbiomes. Pacific Pacific Symposium on Biocomputing, 21:557-567
  3. DL Beck, JA Foster (2015) Machine learning classifiers provide insight into the relationship between microbial communities and bacterial vaginosis. Biomedical Data Mining and Analysis, 8:23, DOI10.1186/s13040-015-0055-3
  1. S Ma, JA Foster, LJ Forney (2015) Network analysis reveals a potentially “evil” alliance of opportunistic pathogens inhibited by a cooperative network. Nature Scientific Reports, 5, 8275–6
  2. IY Zhbannikov, JA Foster (2015) MetaAmp: Analysis high-throughput microbial amplicon sequence data with multiple markers. Bioinformatics, 31(11), 1830–1832. http://doi.org/10.1093/bioinformatics/btv049
  3. DL Beck, C Daniels, JA Foster (2014) Seed: A microbial community visualization tool. Bioinformatics, pii: btu693
  4. J Carter, DL Beck, H Williams, G Dozier, JA Foster (2014) GA-Based Selection of Vaginal Microbiome Features Associated with Bacterial Vaginosis. Genetic and Evolutionary Computation Conference (GECCO), 2014, Vancouver, BC Canada
  5. YS Baker, R Agrawal, JA Foster, DL Beck, G Dozier (2014) Detecting Bacterial Vaginosis Using Machine Learning. ACM Southeast Conference, Springer
  6. JA Foster (2014) Microbial diversity, bar-coding approaches. Encyclopedia of Metagenomics, Springer
  7. YS Baker, R Agrawal, JA Foster, DL Beck, G Dozier (2014) Applying Machine Learning Techniques in Detecting Bacterial Vaginosis. International Conference on Machine Learning and Cybernetics (ICMLC), Lanzhou, China
  8. DL Beck, JA Foster (2014) Machine learning techniques accurately classify microbial communities by bacterial vaginosis characteristics. PLOS One, 9(2):e87830
  9. KA Pattin, AC Greene, RB Altman, LE Hunter, DA Ross, JA Foster, JH Moore (2014) Building the next generation of quantitative biologists. Pacific Symposium in Biocomputing, 2014