Amino Acid k-mer Feature Extraction for Quantitative Antimicrobial Resistance (AMR) Prediction by Machine Learning and Model Interpretation for Biological Insights

Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Differen...

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Bibliographic Details
Published in:Biology (Basel, Switzerland) Switzerland), 2020-11, Vol.9 (11), p.365
Main Authors: ValizadehAslani, Taha, Zhao, Zhengqiao, Sokhansanj, Bahrad A, Rosen, Gail L
Format: Article
Language:English
Subjects:
amino acid
Amino acids
antimicrobial resistance
Computational biology
DNA sequencing
Drug resistance in microorganisms
genome sequencing
k-mer counting
Machine learning
Medical research
Medicine, Experimental
nucleotide
Nucleotide sequencing
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Summary:Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Different feature extraction methods, such as SNP calling and counting nucleotide k-mers have been proposed for presenting DNA sequences to the model. However, there are trade-offs between interpretability, computational complexity and accuracy for different feature extraction methods. In this study, we have proposed a new feature extraction method, counting amino acid k-mers or oligopeptides, which provides easier model interpretation compared to counting nucleotide k-mers and reaches the same or even better accuracy in comparison with different methods. Additionally, we have trained machine learning algorithms using different feature extraction methods and compared the results in terms of accuracy, model interpretability and computational complexity. We have built a new feature selection pipeline for extraction of important features so that new AMR determinants can be discovered by analyzing these features. This pipeline allows the construction of models that only use a small number of features and can predict resistance accurately.
ISSN:2079-7737
2079-7737
DOI:10.3390/biology9110365