國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁 到查詢結果 [ subject:"Genetics and Population Dynamics." ]

切換: 標籤 | MARC模式 | ISBD

Transcriptome analysisintroduction a...

Cellerino, Alessandro.

Transcriptome analysisintroduction and examples from the neurosciences /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Transcriptome analysisby Alessandro Cellerino, Michele Sanguanini.
其他題名:	introduction and examples from the neurosciences /
作者:	Cellerino, Alessandro.
其他作者:	Sanguanini, Michele.
出版者:	Pisa :Scuola Normale Superiore :2018.
面頁冊數:	xiv, 188 p. :digital ;24 cm.
Contained By:	Springer eBooks
標題:	Bioinformatics.
電子資源:	http://dx.doi.org/10.1007/978-88-7642-642-1
ISBN:	9788876426421$q(electronic bk.)

Transcriptome analysisintroduction and examples from the neurosciences /
Cellerino, Alessandro.

Transcriptome analysisintroduction and examples from the neurosciences /[electronic resource] :by Alessandro Cellerino, Michele Sanguanini. - Pisa :Scuola Normale Superiore :2018. - xiv, 188 p. :digital ;24 cm. - CRM series ;17. - CRM series ;13..

Preface -- Introduction: why study transcriptomics? -- 1. Data distribution and visualisation -- 2. Next-generation RNA sequencing -- 3. RNA-seq raw data processing -- 4. Differentially expressed gene detection & analysis -- 5. Unbiased clustering methods -- 6. Knowledge-based clustering methods -- 7. Network analysis -- 8. Mesoscale transcriptome analysis -- 9. Microscale transcriptome analysis -- Bibliography -- Index.

The goal of this book is to be an accessible guide for undergraduate and graduate students to the new field of data-driven biology. Next-generation sequencing technologies have put genome-scale analysis of gene expression into the standard toolbox of experimental biologists. Yet, biological interpretation of high-dimensional data is made difficult by the lack of a common language between experimental and data scientists. By combining theory with practical examples of how specific tools were used to obtain novel insights in biology, particularly in the neurosciences, the book intends to teach students how to design, analyse, and extract biological knowledge from transcriptome sequencing experiments. Undergraduate and graduate students in biomedical and quantitative sciences will benefit from this text as well as academics untrained in the subject.

ISBN: 9788876426421$q(electronic bk.)

Standard No.: 10.1007/978-88-7642-642-1doiSubjects--Topical Terms:

194415
Bioinformatics.

LC Class. No.: QH455 / .C455 2018

Dewey Class. No.: 570.285

Transcriptome analysisintroduction and examples from the neurosciences /
LDR:02299nmm a2200325 a 4500 001 540759
003 DE-He213
005 20190110084906.0
006 m d
007 cr nn 008maaau
008 190308s2018 it s 0 eng d
020 $a 9788876426421$q(electronic bk.)
020 $a 9788876426414$q(paper)
024 7 $a 10.1007/978-88-7642-642-1 $2 doi
035 $a 978-88-7642-642-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QH455 $b .C455 2018
072 7 $a PBW $2 bicssc
072 7 $a MAT003000 $2 bisacsh
082 0 4 $a 570.285 $2 23
090 $a QH455 $b .C393 2018
100 1 $a Cellerino, Alessandro. $3 819209
245 1 0 $a Transcriptome analysis $h [electronic resource] : $b introduction and examples from the neurosciences / $c by Alessandro Cellerino, Michele Sanguanini.
260 $a Pisa : $b Scuola Normale Superiore : $b Imprint: Edizioni della Normale, $c 2018.
300 $a xiv, 188 p. : $b digital ; $c 24 cm.
490 1 $a CRM series ; $v 17
505 0 $a Preface -- Introduction: why study transcriptomics? -- 1. Data distribution and visualisation -- 2. Next-generation RNA sequencing -- 3. RNA-seq raw data processing -- 4. Differentially expressed gene detection & analysis -- 5. Unbiased clustering methods -- 6. Knowledge-based clustering methods -- 7. Network analysis -- 8. Mesoscale transcriptome analysis -- 9. Microscale transcriptome analysis -- Bibliography -- Index.
520 $a The goal of this book is to be an accessible guide for undergraduate and graduate students to the new field of data-driven biology. Next-generation sequencing technologies have put genome-scale analysis of gene expression into the standard toolbox of experimental biologists. Yet, biological interpretation of high-dimensional data is made difficult by the lack of a common language between experimental and data scientists. By combining theory with practical examples of how specific tools were used to obtain novel insights in biology, particularly in the neurosciences, the book intends to teach students how to design, analyse, and extract biological knowledge from transcriptome sequencing experiments. Undergraduate and graduate students in biomedical and quantitative sciences will benefit from this text as well as academics untrained in the subject.
650 0 $a Bioinformatics. $3 194415
650 0 $a Systems biology. $3 282663
650 0 $a Biomathematics. $3 212374
650 0 $a Genetic transcription. $3 200388
650 1 4 $a Mathematics. $3 184409
650 2 4 $a Genetics and Population Dynamics. $3 274842
650 2 4 $a Computational Biology/Bioinformatics. $3 274833
650 2 4 $a Systems Biology. $3 245824
700 1 $a Sanguanini, Michele. $3 819210
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a CRM series ; $v 13. $3 561874
856 4 0 $u http://dx.doi.org/10.1007/978-88-7642-642-1
950 $a Mathematics and Statistics (Springer-11649)