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Machine learning for microbial pheno...

Feldbauer, Roman.

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Machine learning for microbial phenotype prediction

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for microbial phenotype prediction/ by Roman Feldbauer.
Author:	Feldbauer, Roman.
Published:	Wiesbaden :Springer Fachmedien Wiesbaden : : 2016.,
Description:	xiii, 110 p. :ill., digital ;24 cm.
[NT 15003449]:	Microbial Genotypes and Phenotypes -- Basics of Machine Learning -- Phenotype Prediction Packages -- A Model for Intracellular Lifestyle.
Contained By:	Springer eBooks
Subject:	Artificial intelligence - Biological applications. -
Online resource:	http://dx.doi.org/10.1007/978-3-658-14319-0
ISBN:	9783658143190

Machine learning for microbial phenotype prediction
Feldbauer, Roman.

Machine learning for microbial phenotype prediction[electronic resource] /by Roman Feldbauer. - Wiesbaden :Springer Fachmedien Wiesbaden :2016. - xiii, 110 p. :ill., digital ;24 cm. - BestMasters. - BestMasters..

Microbial Genotypes and Phenotypes -- Basics of Machine Learning -- Phenotype Prediction Packages -- A Model for Intracellular Lifestyle.

This thesis presents a scalable, generic methodology for microbial phenotype prediction based on supervised machine learning, several models for biological and ecological traits of high relevance, and the deployment in metagenomic datasets. The results suggest that the presented prediction tool can be used to automatically annotate phenotypes in near-complete microbial genome sequences, as generated in large numbers in current metagenomic studies. Unraveling relationships between a living organism's genetic information and its observable traits is a central biological problem. Phenotype prediction facilitated by machine learning techniques will be a major step forward to creating biological knowledge from big data. Contents Microbial Genotypes and Phenotypes Basics of Machine Learning Phenotype Prediction Packages A Model for Intracellular Lifestyle Target Groups Teachers and students in the fields of bioinformatics, molecular biology and microbiology Executives and specialists in the field of microbiology, computational biology and machine learning About the Author Roman Feldbauer is currently employed at the Austrian Research Institute for Artificial Intelligence (OFAI) and PhD student at the University of Vienna. His research interests are machine learning, data science, bioinformatics, comparative genomics and neuroscience. In one of his current projects he investigates large biological databases in regard to the "curse of dimensionality".

ISBN: 9783658143190

Standard No.: 10.1007/978-3-658-14319-0doiSubjects--Topical Terms:

590657
Artificial intelligence
--Biological applications.

LC Class. No.: QH324.25

Dewey Class. No.: 570.28563

Machine learning for microbial phenotype prediction
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520 $a This thesis presents a scalable, generic methodology for microbial phenotype prediction based on supervised machine learning, several models for biological and ecological traits of high relevance, and the deployment in metagenomic datasets. The results suggest that the presented prediction tool can be used to automatically annotate phenotypes in near-complete microbial genome sequences, as generated in large numbers in current metagenomic studies. Unraveling relationships between a living organism's genetic information and its observable traits is a central biological problem. Phenotype prediction facilitated by machine learning techniques will be a major step forward to creating biological knowledge from big data. Contents Microbial Genotypes and Phenotypes Basics of Machine Learning Phenotype Prediction Packages A Model for Intracellular Lifestyle Target Groups Teachers and students in the fields of bioinformatics, molecular biology and microbiology Executives and specialists in the field of microbiology, computational biology and machine learning About the Author Roman Feldbauer is currently employed at the Austrian Research Institute for Artificial Intelligence (OFAI) and PhD student at the University of Vienna. His research interests are machine learning, data science, bioinformatics, comparative genomics and neuroscience. In one of his current projects he investigates large biological databases in regard to the "curse of dimensionality".
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