Pakket: q2-sample-classifier (2022.11.1-3)
Verwijzigingen voor q2-sample-classifier
Debian bronnen:
Het bronpakket q2-sample-classifier downloaden:
- [q2-sample-classifier_2022.11.1-3.dsc]
- [q2-sample-classifier_2022.11.1.orig.tar.gz]
- [q2-sample-classifier_2022.11.1-3.debian.tar.xz]
Beheerders:
- Debian Med Packaging Team (QA-pagina, Mailarchief)
- Liubov Chuprikova (QA-pagina)
- Steffen Moeller (QA-pagina)
- Étienne Mollier (QA-pagina)
Externe bronnen:
- Homepage [qiime2.org]
Vergelijkbare pakketten:
QIIME 2 plugin for machine learning prediction of sample data
QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results. Key features:
* Integrated and automatic tracking of data provenance * Semantic type system * Plugin system for extending microbiome analysis functionality * Support for multiple types of user interfaces (e.g. API, command line,graphical)
QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis pipeline. QIIME 2 will address many of the limitations of QIIME 1, while retaining the features that makes QIIME 1 a powerful and widely-used analysis pipeline.
QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline. New functionality will regularly become available through QIIME 2 plugins. You can view a list of plugins that are currently available on the QIIME 2 plugin availability page. The future plugins page lists plugins that are being developed.
Microbiome studies often aim to predict outcomes or differentiate samples based on their microbial compositions, tasks that can be efficiently performed by supervised learning methods. The q2-sample-classifier plugin makes these methods more accessible, reproducible, and interpretable to a broad audience of microbiologists, clinicians, and others who wish to utilize supervised learning methods for predicting sample characteristics based on microbiome composition or other "omics" data
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