大葉大學圖書館 |

Machine learning for microbial phenotype prediction[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	570.28563
書名/作者:	Machine learning for microbial phenotype prediction/ by Roman Feldbauer.
作者:	Feldbauer, Roman.
出版者:	Wiesbaden : : Springer Fachmedien Wiesbaden :, 2016.
面頁冊數:	xiii, 110 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Artificial intelligence - Biological applications.
標題:	Machine learning.
標題:	Comparative genomics - Data processing.
標題:	Phenotype.
標題:	Life Sciences.
標題:	Bioinformatics.
標題:	Mathematical and Computational Biology.
標題:	Microbiology.
ISBN:	9783658143190
ISBN:	9783658143183
內容註:	Microbial Genotypes and Phenotypes -- Basics of Machine Learning -- Phenotype Prediction Packages -- A Model for Intracellular Lifestyle.
摘要、提要註:	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".
電子資源:	http://dx.doi.org/10.1007/978-3-658-14319-0

Machine learning for microbial phenotype prediction[electronic resource] /
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:

340377
Artificial intelligence
--Biological applications.

LC Class. No.: QH324.25

Dewey Class. No.: 570.28563

Machine learning for microbial phenotype prediction[electronic resource] /
LDR:02605nmm a2200337 a 4500 001 458403
003 DE-He213
005 20161114111522.0
006 m d
007 cr nn 008maaau
008 170113s2016 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 659597
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 340377
650 0 $a Machine learning. $3 202931
650 0 $a Comparative genomics $x Data processing. $3 659598
650 0 $a Phenotype. $3 416976
650 1 4 $a Life Sciences. $3 463509
650 2 4 $a Bioinformatics. $3 184439
650 2 4 $a Mathematical and Computational Biology. $3 465124
650 2 4 $a Microbiology. $3 182563
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
830 0 $a BestMasters. $3 602104
856 4 0 $u http://dx.doi.org/10.1007/978-3-658-14319-0
950 $a Biomedical and Life Sciences (Springer-11642)