Files and links (1)
Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
Fizzy: feature subset selection for metagenomics
BMC bioinformatics, v 16(1), pp 358-358
04 Nov 2015
PMID: 26538306
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Background: Some of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using alpha- & beta-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.
Results: We 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.
Conclusions: We have made the software implementation of Fizzy available to the public under the GNU GPL license.
Metrics
Details
- Title
- Fizzy: feature subset selection for metagenomics
- Creators
- Gregory Ditzler - University of ArizonaJ. Calvin Morrison - Drexel UniversityYemin Lan - Drexel UniversityGail L. Rosen - Drexel UniversityKent State Univ., Kent, OH (United States)
- Publication Details
- BMC bioinformatics, v 16(1), pp 358-358
- Publisher
- Springer Nature
- Number of pages
- 8
- Grant note
- SC004335 / DoE; United States Department of Energy (DOE) 1120622 / NSF; National Science Foundation (NSF) Drexel's University Research Computing Facility
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000364115500001
- Scopus ID
- 2-s2.0-84946416256
- Other Identifier
- 991019169598304721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Mathematical & Computational Biology