Metadata-driven computational (meta)genomics. A practical machine learning approach

Rumming M (2018)
Bielefeld: Universität Bielefeld.

Download
OA
Bielefeld Dissertation | English
Supervisor
Sczyrba, AlexanderUniBi ; Chindelevitch, Leonid
Abstract
A vast amount of bacterial and archaeal genomic sequences have been generated in the past decade through single cell sequencing and in particular binning of metagenomic sequences, but a detailed characterization of the functional features and observable phenotypes of such novel genomes is mostly unknown and thus missing. Machine learning models are trained on previously annotated organisms in relation to the mentioned traits and can be used for the characterization of so far undiscovered novel microbial organisms. The metadata is also used to enrich microbial community profiles with this kind of information, and a client-side webtool has been developed for comparative visualizations of these profiles.
Year
PUB-ID

Cite this

Rumming M. Metadata-driven computational (meta)genomics. A practical machine learning approach. Bielefeld: Universität Bielefeld; 2018.
Rumming, M. (2018). Metadata-driven computational (meta)genomics. A practical machine learning approach. Bielefeld: Universität Bielefeld.
Rumming, M. (2018). Metadata-driven computational (meta)genomics. A practical machine learning approach. Bielefeld: Universität Bielefeld.
Rumming, M., 2018. Metadata-driven computational (meta)genomics. A practical machine learning approach, Bielefeld: Universität Bielefeld.
M. Rumming, Metadata-driven computational (meta)genomics. A practical machine learning approach, Bielefeld: Universität Bielefeld, 2018.
Rumming, M.: Metadata-driven computational (meta)genomics. A practical machine learning approach. Universität Bielefeld, Bielefeld (2018).
Rumming, Madis. Metadata-driven computational (meta)genomics. A practical machine learning approach. Bielefeld: Universität Bielefeld, 2018.
All files available under the following license(s):
Copyright Statement:
This Item is protected by copyright and/or related rights. [...]
Main File(s)
Access Level
OA Open Access
Last Uploaded
2018-04-04T14:22:16Z
MD5 Checksum
4022abf27493a02b3a9230239fc901fe

This data publication is cited in the following publications:
This publication cites the following data publications:

Export

0 Marked Publications

Open Data PUB

Search this title in

Google Scholar