Discriminative Pattern Discovery on Biological Networks

Lieferzeit: Lieferbar innerhalb 14 Tagen

53,49 

SpringerBriefs in Computer Science

ISBN: 3319634763
ISBN 13: 9783319634760
Autor: Fassetti, Fabio/Rombo, Simona E/Serrao, Cristina
Verlag: Springer Verlag GmbH
Umfang: x, 45 S., 4 s/w Illustr., 45 p. 4 illus.
Erscheinungsdatum: 11.09.2017
Auflage: 1/2017
Produktform: Kartoniert
Einband: Kartoniert

Provides a review of biological networks as a model for analysis, presenting and discussing a number of analysesDescribes techniques for discovering exceptional patterns, with a focus on discriminative patternsDiscusses a method for discovering discriminative patterns, which generates a database of networks representing a sample setIncludes supplementary material: sn.pub/extras

Artikelnummer: 2496711 Kategorie:

Beschreibung

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.

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