ArticleBioinformatics (Oxford, England)2024
The Naïve Bayes classifier++ for metagenomic taxonomic classification-query evaluation.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- An explainable machine learning framework integrating clinical and lipidomic signatures for early severity stratification of hypertriglyceridemic pancreatitis.Frontiers in medicine · 2026Article
- Article
- A predictive study of glycaemic reversal in Chinese individuals with prediabetes based on machine learning: a 5-year cohort study.Frontiers in endocrinology · 2026Article
- Integrated multi-omics profiling reveals shared mechanistic pathways in asthma-allergic rhinitis comorbidity: A hybrid machine learning framework leveraging Mendelian randomization for precision diagnostics.The World Allergy Organization journal · 2025Article
- CAMI Benchmarking Portal: online evaluation and ranking of metagenomic software.Nucleic acids research · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
motivationThis study examines the query performance of the NBC++ (Incremental Naive Bayes Classifier) program for variations in canonicality, k-mer size, databases, and input sample data size. We demonstrate that both NBC++ and Kraken2 are influenced by database depth, with macro measures improving as depth increases. However, fully capturing the diversity of life, especially viruses, remains a challenge.
resultsNBC++ can competitively profile the superkingdom content of metagenomic samples using a small training database. NBC++ spends less time training and can use a fraction of the memory than Kraken2 but at the cost of long querying time. Major NBC++ enhancements include accommodating canonical k-mer storage (leading to significant storage savings) and adaptable and optimized memory allocation that accelerates query analysis and enables the software to be run on nearly any system. Additionally, the output now includes log-likelihood values for each training genome, providing users with valuable confidence information. AVAILABILITY AND IMPLEMENTATION: Source code and Dockerfile are available at http://github.com/EESI/Naive_Bayes.
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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.