國立高雄大學圖資館 |

Language: English

Back

Machine learning for microbial pheno...

Feldbauer, Roman.

Machine learning for microbial phenotype prediction

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for microbial phenotype predictionby Roman Feldbauer.
Author:	Feldbauer, Roman.
Published:	Wiesbaden :Springer Fachmedien Wiesbaden :2016.
Description:	xiii, 110 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Artificial intelligenceBiological applications.
Online resource:	http://dx.doi.org/10.1007/978-3-658-14319-0
ISBN:	9783658143190$q(electronic bk.)

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$q(electronic bk.)

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

238843
Artificial intelligence
--Biological applications.

LC Class. No.: QH324.25

Dewey Class. No.: 570.28563

Machine learning for microbial phenotype prediction
LDR:02605nmm a2200337 a 4500 001 490237
003 DE-He213
005 20161114111522.0
006 m d
007 cr nn 008maaau
008 170118s2016 gw s 0 eng d
020 $a 9783658143190$q(electronic bk.)
020 $a 9783658143183$q(paper)
024 7 $a 10.1007/978-3-658-14319-0 $2 doi
035 $a 978-3-658-14319-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QH324.25
072 7 $a PSD $2 bicssc
072 7 $a UB $2 bicssc
072 7 $a SCI056000 $2 bisacsh
082 0 4 $a 570.28563 $2 23
090 $a QH324.25 $b .F312 2016
100 1 $a Feldbauer, Roman. $3 749570
245 1 0 $a Machine learning for microbial phenotype prediction $h [electronic resource] / $c by Roman Feldbauer.
260 $a Wiesbaden : $b Springer Fachmedien Wiesbaden : $b Imprint: Springer Spektrum, $c 2016.
300 $a xiii, 110 p. : $b ill., digital ; $c 24 cm.
490 1 $a BestMasters
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 Artificial intelligence $x Biological applications. $3 238843
650 0 $a Machine learning. $3 188639
650 0 $a Comparative genomics $x Data processing. $3 749571
650 0 $a Phenotype. $3 265390
650 1 4 $a Life Sciences. $3 273679
650 2 4 $a Bioinformatics. $3 194415
650 2 4 $a Mathematical and Computational Biology. $3 514442
650 2 4 $a Microbiology. $3 192943
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a BestMasters. $3 676593
856 4 0 $u http://dx.doi.org/10.1007/978-3-658-14319-0
950 $a Biomedical and Life Sciences (Springer-11642)