ArticleScientific reports2017
PaPrBaG: A machine learning approach for the detection of novel pathogens from NGS data.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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41 citing papers in PubMed, 107 citations in OpenAlex.
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- Identification of human pathogens in soil by virulence gene-based machine learning method.Eco-Environment & Health · 2025Article
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- BacSPaD: A Robust Bacterial Strains' Pathogenicity Resource Based on Integrated and Curated Genomic Metadata.Pathogens (Basel, Switzerland) · 2024Article
- Pathogen-Agnostic Advanced Molecular Diagnostic Testing for Difficult-to-Diagnose Clinical Syndromes-Results of an Emerging Infections Network Survey of Frontline US Infectious Disease Clinicians, May 2023.Open forum infectious diseases · 2024Article
- Application of Next-Generation Sequencing (NGS) Techniques for Selected Companion Animals.Animals : an open access journal from MDPI · 2024Review
- MetagenomicKG: a knowledge graph for metagenomic applications.bioRxiv : the preprint server for biology · 2024Article
- Artificial intelligence applications in the diagnosis and treatment of bacterial infections.Frontiers in microbiology · 2024Review
- The potential application of artificial intelligence in veterinary clinical practice and biomedical research.Frontiers in veterinary science · 2024Article
- DCiPatho: deep cross-fusion networks for genome scale identification of pathogens.Briefings in bioinformatics · 2023Article
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
- Reference-Free Plant Disease Detection Using Machine Learning and Long-Read Metagenomic Sequencing.Applied and environmental microbiology · 2023Article
- MP4: a machine learning based classification tool for prediction and functional annotation of pathogenic proteins from metagenomic and genomic datasets.BMC bioinformatics · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The reliable detection of novel bacterial pathogens from next-generation sequencing data is a key challenge for microbial diagnostics. Current computational tools usually rely on sequence similarity and often fail to detect novel species when closely related genomes are unavailable or missing from the reference database. Here we present the machine learning based approach PaPrBaG (Pathogenicity Prediction for Bacterial Genomes). PaPrBaG overcomes genetic divergence by training on a wide range of species with known pathogenicity phenotype. To that end we compiled a comprehensive list of pathogenic and non-pathogenic bacteria with human host, using various genome metadata in conjunction with a rule-based protocol. A detailed comparative study reveals that PaPrBaG has several advantages over sequence similarity approaches. Most importantly, it always provides a prediction whereas other approaches discard a large number of sequencing reads with low similarity to currently known reference genomes. Furthermore, PaPrBaG remains reliable even at very low genomic coverages. CombiningPaPrBaG with existing approaches further improves prediction results.
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