ArticleBMC bioinformatics2015
Fizzy: feature subset selection for metagenomics.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa.PLoS computational biology · 2022Pooled it
- Oral microbiome classification of elevated salivary glucose in a large adolescent Kuwaiti population.Frontiers in microbiology · 2026Article
- Gastrointestinal microbiota and inflammasomes interplay in health and disease: a gut feeling.Gut · 2025Review
- Article
- Exploring and exploiting the rice phytobiome to tackle climate change challenges.Plant communications · 2024Review
- Hematological indicator-based machine learning models for preoperative prediction of lymph node metastasis in cervical cancer.Frontiers in oncology · 2024Article
- MetaAnalyst: a user-friendly tool for metagenomic biomarker detection and phenotype classification.BMC medical research methodology · 2022Article
- Machine learning for data integration in human gut microbiome.Microbial cell factories · 2022Review
- Machine learning and deep learning applications in microbiome research.ISME communications · 2022Review
- Article
- Microbiome Preprocessing Machine Learning Pipeline.Frontiers in immunology · 2021Article
- Metagenomic Geolocation Prediction Using an Adaptive Ensemble Classifier.Frontiers in genetics · 2021Article
- Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.Frontiers in microbiology · 2021Review
- A novel riboswitch classification based on imbalanced sequences achieved by machine learning.PLoS computational biology · 2020Article
- MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction.Methods (San Diego, Calif.) · 2019Article
- Biomarker discovery in inflammatory bowel diseases using network-based feature selection.PloS one · 2019Article
- A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction.Frontiers in genetics · 2019Review
- Taxonomy-aware feature engineering for microbiome classification.BMC bioinformatics · 2018Article
- Opportunities and obstacles for deep learning in biology and medicine.Journal of the Royal Society, Interface · 2018Review
- The parameter sensitivity of random forests.BMC bioinformatics · 2016Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSome of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using α- & β-diversity. Feature subset selection--a sub-field of machine learning--can also provide a unique insight into the differences between metagenomic or 16S phenotypes. In particular, feature subset selection methods can obtain the operational taxonomic units (OTUs), or functional features, that have a high-level of influence on the condition being studied. For example, in a previous study we have used information-theoretic feature selection to understand the differences between protein family abundances that best discriminate between age groups in the human gut microbiome.
resultsWe have developed a new Python command line tool, which is compatible with the widely adopted BIOM format, for microbial ecologists that implements information-theoretic subset selection methods for biological data formats. We demonstrate the software tools capabilities on publicly available datasets.
conclusionsWe have made the software implementation of Fizzy available to the public under the GNU GPL license. The standalone implementation can be found at http://github.com/EESI/Fizzy.
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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.