In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 17, No. 3 ( 2021-3-29), p. e1008857-
Abstract:
To better combat the expansion of antibiotic resistance in pathogens, new compounds, particularly those with novel mechanisms-of-action [MOA], represent a major research priority in biomedical science. However, rediscovery of known antibiotics demonstrates a need for approaches that accurately identify potential novelty with higher throughput and reduced labor. Here we describe an explainable artificial intelligence classification methodology that emphasizes prediction performance and human interpretability by using a Hierarchical Ensemble of Classifiers model optimized with a novel feature selection algorithm called Clairvoyance ; collectively referred to as a CoHEC model. We evaluated our methods using whole transcriptome responses from Escherichia coli challenged with 41 known antibiotics and 9 crude extracts while depositing 122 transcriptomes unique to this study. Our CoHEC model can properly predict the primary MOA of previously unobserved compounds in both purified forms and crude extracts at an accuracy above 99%, while also correctly identifying darobactin, a newly discovered antibiotic, as having a novel MOA. In addition, we deploy our methods on a recent E . coli transcriptomics dataset from a different strain and a Mycobacterium smegmatis metabolomics timeseries dataset showcasing exceptionally high performance; improving upon the performance metrics of the original publications. We not only provide insight into the biological interpretation of our model but also that the concept of MOA is a non-discrete heuristic with diverse effects for different compounds within the same MOA, suggesting substantial antibiotic diversity awaiting discovery within existing MOA.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1008857
DOI:
10.1371/journal.pcbi.1008857.g001
DOI:
10.1371/journal.pcbi.1008857.g002
DOI:
10.1371/journal.pcbi.1008857.g003
DOI:
10.1371/journal.pcbi.1008857.t001
DOI:
10.1371/journal.pcbi.1008857.t002
DOI:
10.1371/journal.pcbi.1008857.t003
DOI:
10.1371/journal.pcbi.1008857.s001
DOI:
10.1371/journal.pcbi.1008857.s002
DOI:
10.1371/journal.pcbi.1008857.s003
DOI:
10.1371/journal.pcbi.1008857.s004
DOI:
10.1371/journal.pcbi.1008857.s005
DOI:
10.1371/journal.pcbi.1008857.s006
DOI:
10.1371/journal.pcbi.1008857.s007
DOI:
10.1371/journal.pcbi.1008857.s008
DOI:
10.1371/journal.pcbi.1008857.s009
DOI:
10.1371/journal.pcbi.1008857.s010
DOI:
10.1371/journal.pcbi.1008857.s011
DOI:
10.1371/journal.pcbi.1008857.s012
DOI:
10.1371/journal.pcbi.1008857.s013
DOI:
10.1371/journal.pcbi.1008857.s014
DOI:
10.1371/journal.pcbi.1008857.s015
DOI:
10.1371/journal.pcbi.1008857.s016
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2193340-6
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