- Academic Editor
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Background: There are several antibiotic resistance genes (ARG) for the
Escherichia coli (E. coli) bacteria that cause urinary tract infections
(UTI), and it is therefore important to identify these ARG. Artificial
Intelligence (AI) has been used previously in the field of gene expression data,
but never adopted for the detection and classification of bacterial ARG. We
hypothesize, if the data is correctly conferred, right features are selected, and
Deep Learning (DL) classification models are optimized, then (i) non-linear DL
models would perform better than Machine Learning (ML) models, (ii) leads to higher accuracy, (iii) can identify the hub genes,
and, (iv) can identify gene pathways accurately. We have therefore
designed aiGeneR, the first of its kind system that uses DL-based models
to identify ARG in E. coli in gene expression data.
Methodology: The aiGeneR consists of a tandem connection of quality
control embedded with feature extraction and AI-based classification of ARG. We
adopted a cross-validation approach to evaluate the performance of aiGeneR using
accuracy, precision, recall, and F1-score. Further, we analyzed the effect of
sample size ensuring generalization of models and compare against the power
analysis. The aiGeneR was validated scientifically and biologically for hub genes
and pathways. We benchmarked aiGeneR against two linear and two other non-linear
AI models. Results: The aiGeneR identifies tetM (an ARG) and showed an
accuracy of 93% with area under the curve (AUC) of 0.99 (p