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E-RESOURCE
Author Feldbauer, Roman, author.

Title Machine learning for microbial phenotype prediction / Roman Feldbauer.

Published Wiesbaden : SpringerSpektrum, 2016.

Copies

Location Call No. Status
 UniM INTERNET resource    AVAILABLE
Physical description 1 online resource (xiii, 110 pages) : illustrations.
Series BestMasters
BestMasters.
Springer English/International eBooks 2016 - Full Set
Springer Biomedical and Life Sciences eBooks 2016 English+International
Bibliography Includes bibliographical references.
Contents Microbial Genotypes and Phenotypes -- Basics of Machine Learning -- Phenotype Prediction Packages -- A Model for Intracellular Lifestyle.
Summary 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". .
Other author SpringerLink issuing body.
Subject Artificial intelligence -- Biological applications.
Machine learning.
Comparative genomics -- Data processing.
Phenotype.
Electronic books.
ISBN 9783658143190 (electronic bk.)
3658143193 (electronic bk.)
9783658143183 (print)