語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Machine Learning for Microbial Pheno...
~
Feldbauer, Roman.
Machine Learning for Microbial Phenotype Prediction
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Machine Learning for Microbial Phenotype Prediction/ by Roman Feldbauer.
作者:
Feldbauer, Roman.
面頁冊數:
XIII, 110 p. 29 illus.online resource. :
Contained By:
Springer Nature eBook
標題:
Bioinformatics. -
電子資源:
https://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. - 1st ed. 2016. - XIII, 110 p. 29 illus.online resource. - BestMasters,2625-3577. - 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:
583857
Bioinformatics.
LC Class. No.: QH324.2-324.25
Dewey Class. No.: 570.285
Machine Learning for Microbial Phenotype Prediction
LDR
:02988nam a22004095i 4500
001
975278
003
DE-He213
005
20200702085303.0
007
cr nn 008mamaa
008
201211s2016 gw | s |||| 0|eng d
020
$a
9783658143190
$9
978-3-658-14319-0
024
7
$a
10.1007/978-3-658-14319-0
$2
doi
035
$a
978-3-658-14319-0
050
4
$a
QH324.2-324.25
072
7
$a
PSD
$2
bicssc
072
7
$a
SCI056000
$2
bisacsh
072
7
$a
PSD
$2
thema
072
7
$a
UB
$2
thema
082
0 4
$a
570.285
$2
23
100
1
$a
Feldbauer, Roman.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1109865
245
1 0
$a
Machine Learning for Microbial Phenotype Prediction
$h
[electronic resource] /
$c
by Roman Feldbauer.
250
$a
1st ed. 2016.
264
1
$a
Wiesbaden :
$b
Springer Fachmedien Wiesbaden :
$b
Imprint: Springer Spektrum,
$c
2016.
300
$a
XIII, 110 p. 29 illus.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
BestMasters,
$x
2625-3577
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.
$3
583857
650
0
$a
Biomathematics.
$3
527725
650
0
$a
Microbiology.
$3
591510
650
2 4
$a
Mathematical and Computational Biology.
$3
786706
710
2
$a
SpringerLink (Online service)
$3
593884
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
2625-3577
$3
1253531
856
4 0
$u
https://doi.org/10.1007/978-3-658-14319-0
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)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入