ArticleBriefings in bioinformatics2021
DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Deep learning insights into β-lactamase dynamics and resistance evolution.The Biochemical journal · 2026Review
- NDM-5-carried outer membrane vesicles impair the efficacy of antibiotics against bacterial infections.Antimicrobial agents and chemotherapy · 2025Article
- INTEDE 2.0: the metabolic roadmap of drugs.Nucleic acids research · 2024Article
- Article
- A review of deep learning applications in human genomics using next-generation sequencing data.Human genomics · 2022Review
- β-LacFamPred: An online tool for prediction and classification of β-lactamase class, subclass, and family.Frontiers in microbiology · 2022Article
- DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites.Frontiers in cell and developmental biology · 2021Article
- A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning.Frontiers in pharmacology · 2021Review
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 3 countries.
Funding
Abstract
Beta-lactamases (BLs) are enzymes localized in the periplasmic space of bacterial pathogens, where they confer resistance to beta-lactam antibiotics. Experimental identification of BLs is costly yet crucial to understand beta-lactam resistance mechanisms. To address this issue, we present DeepBL, a deep learning-based approach by incorporating sequence-derived features to enable high-throughput prediction of BLs. Specifically, DeepBL is implemented based on the Small VGGNet architecture and the TensorFlow deep learning library. Furthermore, the performance of DeepBL models is investigated in relation to the sequence redundancy level and negative sample selection in the benchmark dataset. The models are trained on datasets of varying sequence redundancy thresholds, and the model performance is evaluated by extensive benchmarking tests. Using the optimized DeepBL model, we perform proteome-wide screening for all reviewed bacterium protein sequences available from the UniProt database. These results are freely accessible at the DeepBL webserver at http://deepbl.erc.monash.edu.au/.
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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.