Author

COMPARE ML-AMR group

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Biology Disciplines

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Microbiology· 2

Signature Assays, Organisms & Methods

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Assay:whole-genome sequencing· 2 Organism:bacteria (other)· 1 Research Focus:database / resource· 1 Organism:E. coli· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

C. Schultsz· 2 shared I. Csabai· 2 shared O. Lund· 2 shared S. Matamoros· 2 shared B. A. Pataki· 1 shared B. C. L. van der Putten· 1 shared B. Pataki· 1 shared C. Amid· 1 shared

Activity by Year

2019-2020 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersMicrobiology

C. Schultsz

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2 papers

Recent·Most cited

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

B. A. Pataki, S. Matamoros, B. C. L. van der Putten, D. Remondini, E. Giampieri, D. Aytan-Aktug, R. S. Hendriksen, O. Lund, I. Csabai, C. Schultsz, COMPARE ML-AMR group

This paper predicts ciprofloxacin minimum inhibitory concentration in Escherichia coli using machine learning based on genome-wide mutation profiles and antimicrobial resistance gene presence.

bioRxiv · Jan 17, 2020Microbiology

Accelerating surveillance and research of antimicrobial resistance - an online repository for sharing of antimicrobial susceptibility data associated with whole genome sequences

S. Matamoros, R. Hendriksen, B. Pataki, N. Pakseresht, M. Rossello, N. Silvester, C. Amid, COMPARE ML-AMR group, G. Cochrane, I. Csabai, O. Lund, C. Schultsz

The paper introduces an online repository for sharing antimicrobial susceptibility data linked to whole genome sequences to enhance AMR surveillance and research.

bioRxiv · Jan 28, 2019Microbiology