Machine-learning to predict and understand the zoonotic threat of E.Coli 0157 isolates

Status	Finished
Effective start/end date	1/10/17 → 31/01/21

Analysis of E. coli O157 strains in cattle and humans between Scotland and England & Wales: implications for human health
Chase-Topping, M., Dallman, T., Allison, L., Lupolova, N., Matthews, L., Mitchell, S., Banks, C., Prentice, J., Brown, H., Tongue, S., Henry, M. K., Evans, J., Gunn, G., Hoyle, D., McNeilly, T. N., Fitzgerald, S., Smith-Palmer, A., Shaaban, S., Holmes, A., Hanson, M., & 4 othersWoolhouse, M., Didelot, X., Jenkins, C. & Gally, D., Sept 2023, In: Microbial Genomics. 9, 9, p. 1-12 12 p., 001090.
Research output: Contribution to journal › Article › peer-review

Open Access
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Predicting Host Association for Shiga Toxin-Producing E. coli Serogroups by Machine Learning
Lupolova, N., Chalka, A. & Gally, D. L., 12 Mar 2021, (E-pub ahead of print) Shiga Toxin-Producing E. coli . Walker, J. M., Schuller, S. & Bielaszewska, M. (eds.). Springer, Vol. 2291. p. 99-117 19 p. (Methods in molecular biology (Clifton, N.J.)).
Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review
A guide to machine learning for bacterial host attribution using genome sequence data
Lupolova, N., Lycett, S. & Gally, D., 28 Nov 2019, (E-pub ahead of print) In: Microbial Genomics.
Research output: Contribution to journal › Article › peer-review

Open Access
File

Project Details