ARG-SHINE: Improve antibiotic resistance class prediction by integrating sequence homology, functional information and deep convolutional neural network
NAR Genomics and Bioinformatics, ISSN: 2631-9268, Vol: 3, Issue: 3, Page: lqab066
2021
- 8Citations
- 23Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations8
- Citation Indexes8
- Captures23
- Readers23
- 23
Article Description
Antibiotic resistance in bacteria limits the effect of corresponding antibiotics, and the classification of antibiotic resistance genes (ARGs) is important for the treatment of bacterial infections and for understanding the dynamics of microbial communities. Although several methods have been developed to classify ARGs, none of them work well when the ARGs diverge from those in the reference ARG databases. We develop a novel method, ARG-SHINE, for ARG classification. ARG-SHINE utilizes state-of-the-art learning to rank machine learning approach to ensemble three component methods with different features, including sequence homology, protein domain/family/motif and raw amino acid sequences for the deep convolutional neural network. Compared with other methods, ARG-SHINE achieves better performance on two benchmark datasets in terms of accuracy, macro-average f1-score and weighted-average f1-score. ARG-SHINE is used to classify newly discovered ARGs through functional screening and achieves high prediction accuracy. ARG-SHINE is freely available at https://github.com/ziyewang/ARG_SHINE.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85123225434&origin=inward; http://dx.doi.org/10.1093/nargab/lqab066; http://www.ncbi.nlm.nih.gov/pubmed/34377977; https://academic.oup.com/nargab/article/doi/10.1093/nargab/lqab066/6342217; https://dx.doi.org/10.1093/nargab/lqab066; https://academic.oup.com/nargab/article/3/3/lqab066/6342217
Oxford University Press (OUP)
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