ArticleFrontiers in genetics2021
Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- AMR-meta: a k-mer and metafeature approach to classify antimicrobial resistance from high-throughput short-read metagenomics data.GigaScience · 2022Article
- Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing.Briefings in bioinformatics · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Antimicrobial resistance (AMR) is a significant and growing public health threat. Sequencing of bacterial isolates is becoming more common, and therefore automatic identification of resistant bacterial strains is of pivotal importance for efficient, wide-spread AMR detection. To support this approach, several AMR databases and gene identification algorithms have been recently developed. A key problem in AMR detection, however, is the need for computational approaches detecting potential novel AMR genes or variants, which are not included in the reference databases. Toward this direction, here we study the relation between AMR and relative solvent accessibility (RSA) of protein variants from an
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Registered trials
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