ArticlePLoS computational biology2021
Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 34 citations in OpenAlex.
- Next-Generation Target Discovery in ESKAPE Pathogens: An AI-Driven Framework from Omics-Based to Systems-Level Modeling and Clinical Translation.Antibiotics (Basel, Switzerland) · 2026Review
- Integrating explainable AI and One Health: a new frontier in combating infectious diseases.EBioMedicine · 2026Article
- Artificial intelligence drives the identification and screening of novel antibiotics and antimicrobial peptides.Briefings in bioinformatics · 2026Review
- Artificial Intelligence and the Discovery of Antibiotics: Reinventing with Opportunities, Challenges, and Clinical Translation.Antibiotics (Basel, Switzerland) · 2026Review
- Integrated omics reveal a unique antibacterial mechanism of action for the small molecule HSI#6.Current research in microbial sciences · 2026Article
- Mechanistic Studies of Antibacterial PNAs by RNA-Seq Analysis.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Molecular response of Pseudomonas aeruginosa to rosemary essential oil and chlorogenic acid at subinhibitory concentration revealed by comparative transcriptomic approach.Scientific reports · 2025Article
- Beyond Antibiotics: Repurposing Non-Antibiotic Drugs as Novel Antibacterial Agents to Combat Resistance.International journal of molecular sciences · 2025Review
- Analyzing the Challenges and Opportunities Associated With Harnessing New Antibiotics From the Fungal Microbiome.MicrobiologyOpen · 2025Review
- Frontiers in superbug management: innovating approaches to combat antimicrobial resistance.Archives of microbiology · 2025Review
- Perturbation-specific transcriptional mapping for unbiased target elucidation of antibiotics.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Unveiling the microbial realm with VEBA 2.0: a modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic and viral multi-omics from either short- or long-read sequencing.Nucleic acids research · 2024Article
- Host-microbiome associations in saliva predict COVID-19 severity.PNAS nexus · 2024Article
- Unveiling the Microbial Realm with VEBA 2.0: A modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic, and viral multi-omics from either short- or long-read sequencing.bioRxiv : the preprint server for biology · 2024Article
- Validating a Promoter Library for Application in Plasmid-Based Diatom Genetic Engineering.ACS synthetic biology · 2023Article
- Genus-Wide Transcriptional Landscapes Reveal Correlated Gene Networks Underlying Microevolutionary Divergence in Diatoms.Molecular biology and evolution · 2023Article
- Leveraging artificial intelligence in the fight against infectious diseases.Science (New York, N.Y.) · 2023Review
- Differential network analysis of oral microbiome metatranscriptomes identifies community scale metabolic restructuring in dental caries.PNAS nexus · 2022Article
- VEBA: a modular end-to-end suite for in silico recovery, clustering, and analysis of prokaryotic, microeukaryotic, and viral genomes from metagenomes.BMC bioinformatics · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.