Machine Learning for Microbial Phenotype Prediction

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Feldbauer, Roman (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Spektrum, 2016.
Edición:1st ed. 2016.
Colección:BestMasters,
Temas:
Bioinformatics.
Biomathematics.
Microbiology.
Mathematical and Computational Biology.
Acceso en línea:Texto Completo

MARC

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264 1 |a Wiesbaden :  |b Springer Fachmedien Wiesbaden :  |b Imprint: Springer Spektrum,  |c 2016. 
300 |a XIII, 110 p. 29 illus.  |b online resource. 
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490 1 |a BestMasters,  |x 2625-3615 
505 0 |a Microbial Genotypes and Phenotypes -- Basics of Machine Learning -- Phenotype Prediction Packages -- A Model for Intracellular Lifestyle. 
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". . 
650 0 |a Bioinformatics. 
650 0 |a Biomathematics. 
650 0 |a Microbiology. 
650 1 4 |a Bioinformatics. 
650 2 4 |a Mathematical and Computational Biology. 
650 2 4 |a Microbiology. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783658143183 
776 0 8 |i Printed edition:  |z 9783658143206 
830 0 |a BestMasters,  |x 2625-3615 
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912 |a ZDB-2-SBL 
912 |a ZDB-2-SXB 
950 |a Biomedical and Life Sciences (SpringerNature-11642) 
950 |a Biomedical and Life Sciences (R0) (SpringerNature-43708) 

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