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Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach
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 ident...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8031737/ https://www.ncbi.nlm.nih.gov/pubmed/33780444 http://dx.doi.org/10.1371/journal.pcbi.1008857 |
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author | Espinoza, Josh L. Dupont, Chris L. O’Rourke, Aubrie Beyhan, Sinem Morales, Pavel Spoering, Amy Meyer, Kirsten J. Chan, Agnes P. Choi, Yongwook Nierman, William C. Lewis, Kim Nelson, Karen E. |
author_facet | Espinoza, Josh L. Dupont, Chris L. O’Rourke, Aubrie Beyhan, Sinem Morales, Pavel Spoering, Amy Meyer, Kirsten J. Chan, Agnes P. Choi, Yongwook Nierman, William C. Lewis, Kim Nelson, Karen E. |
author_sort | Espinoza, Josh L. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8031737 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-80317372021-04-14 Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach Espinoza, Josh L. Dupont, Chris L. O’Rourke, Aubrie Beyhan, Sinem Morales, Pavel Spoering, Amy Meyer, Kirsten J. Chan, Agnes P. Choi, Yongwook Nierman, William C. Lewis, Kim Nelson, Karen E. PLoS Comput Biol Research Article 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. Public Library of Science 2021-03-29 /pmc/articles/PMC8031737/ /pubmed/33780444 http://dx.doi.org/10.1371/journal.pcbi.1008857 Text en © 2021 Espinoza et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Espinoza, Josh L. Dupont, Chris L. O’Rourke, Aubrie Beyhan, Sinem Morales, Pavel Spoering, Amy Meyer, Kirsten J. Chan, Agnes P. Choi, Yongwook Nierman, William C. Lewis, Kim Nelson, Karen E. Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach |
title | Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach |
title_full | Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach |
title_fullStr | Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach |
title_full_unstemmed | Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach |
title_short | Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach |
title_sort | predicting antimicrobial mechanism-of-action from transcriptomes: a generalizable explainable artificial intelligence approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8031737/ https://www.ncbi.nlm.nih.gov/pubmed/33780444 http://dx.doi.org/10.1371/journal.pcbi.1008857 |
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