東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Discriminative pattern discovery on ...

Fassetti, Fabio.

Linked to FindBook

Google Book

Amazon

博客來

Discriminative pattern discovery on biological networks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Discriminative pattern discovery on biological networks/ by Fabio Fassetti, Simona E. Rombo, Cristina Serrao.
Author:	Fassetti, Fabio.
other author:	Rombo, Simona E.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	x, 45 p. :ill., digital ;24 cm.
[NT 15003449]:	Part I: Biological Networks -- Data Sources and Models -- Problems and Techniques -- Part II: Pattern Mining -- Exceptional Pattern Discovery -- Discriminating Graph Pattern Mining from Gene Expression Data.
Contained By:	Springer eBooks
Subject:	Biological systems - Simulation methods. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-63477-7
ISBN:	9783319634777

Discriminative pattern discovery on biological networks
Fassetti, Fabio.

Discriminative pattern discovery on biological networks[electronic resource] /by Fabio Fassetti, Simona E. Rombo, Cristina Serrao. - Cham :Springer International Publishing :2017. - x, 45 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

Part I: Biological Networks -- Data Sources and Models -- Problems and Techniques -- Part II: Pattern Mining -- Exceptional Pattern Discovery -- Discriminating Graph Pattern Mining from Gene Expression Data.

This work provides a review of biological networks as a model for analysis, presenting and discussing a number of illuminating analyses. Biological networks are an effective model for providing insights about biological mechanisms. Networks with different characteristics are employed for representing different scenarios. This powerful model allows analysts to perform many kinds of analyses which can be mined to provide interesting information about underlying biological behaviors. The text also covers techniques for discovering exceptional patterns, such as a pattern accounting for local similarities and also collaborative effects involving interactions between multiple actors (for example genes) Among these exceptional patterns, of particular interest are discriminative patterns, namely those which are able to discriminate between two input populations (for example healthy/unhealthy samples) In addition, the work includes a discussion on the most recent proposal on discovering discriminative patterns, in which there is a labeled network for each sample, resulting in a database of networks representing a sample set. This enables the analyst to achieve a much finer analysis than with traditional techniques, which are only able to consider an aggregated network of each population.

ISBN: 9783319634777

Standard No.: 10.1007/978-3-319-63477-7doiSubjects--Topical Terms:

653277
Biological systems
--Simulation methods.

LC Class. No.: QH324.2

Dewey Class. No.: 570.113

Discriminative pattern discovery on biological networks
LDR:02554nmm a2200337 a 4500 001 2108858
003 DE-He213
005 20170901184910.0
006 m d
007 cr nn 008maaau
008 180519s2017 gw s 0 eng d
020 $a 9783319634777 $q (electronic bk.)
020 $a 9783319634760 $q (paper)
024 7 $a 10.1007/978-3-319-63477-7 $2 doi
035 $a 978-3-319-63477-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QH324.2
072 7 $a PSA $2 bicssc
072 7 $a UB $2 bicssc
072 7 $a COM014000 $2 bisacsh
082 0 4 $a 570.113 $2 23
090 $a QH324.2 $b .F249 2017
100 1 $a Fassetti, Fabio. $3 3258584
245 1 0 $a Discriminative pattern discovery on biological networks $h [electronic resource] / $c by Fabio Fassetti, Simona E. Rombo, Cristina Serrao.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a x, 45 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in computer science, $x 2191-5768
505 0 $a Part I: Biological Networks -- Data Sources and Models -- Problems and Techniques -- Part II: Pattern Mining -- Exceptional Pattern Discovery -- Discriminating Graph Pattern Mining from Gene Expression Data.
520 $a This work provides a review of biological networks as a model for analysis, presenting and discussing a number of illuminating analyses. Biological networks are an effective model for providing insights about biological mechanisms. Networks with different characteristics are employed for representing different scenarios. This powerful model allows analysts to perform many kinds of analyses which can be mined to provide interesting information about underlying biological behaviors. The text also covers techniques for discovering exceptional patterns, such as a pattern accounting for local similarities and also collaborative effects involving interactions between multiple actors (for example genes) Among these exceptional patterns, of particular interest are discriminative patterns, namely those which are able to discriminate between two input populations (for example healthy/unhealthy samples) In addition, the work includes a discussion on the most recent proposal on discovering discriminative patterns, in which there is a labeled network for each sample, resulting in a database of networks representing a sample set. This enables the analyst to achieve a much finer analysis than with traditional techniques, which are only able to consider an aggregated network of each population.
650 0 $a Biological systems $x Simulation methods. $3 653277
650 0 $a Pattern recognition systems. $3 527885
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Computational Biology/Bioinformatics. $3 898313
650 2 4 $a Pattern Recognition. $3 891045
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Bioinformatics. $3 553671
650 2 4 $a Gene Expression. $3 600530
700 1 $a Rombo, Simona E. $3 3258585
700 1 $a Serrao, Cristina. $3 3258586
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in computer science. $3 1567571
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-63477-7
950 $a Computer Science (Springer-11645)