Name: Discriminative Pattern Discovery on Biological Networks (Springerbriefs in Computer Science) (en Inglés)
Price: 1088.89 MXN
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Author: Fabio Fassetti; Simona E. Rombo; Cristina Serrao
ISBN: 9783319634760

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portada Discriminative Pattern Discovery on Biological Networks (Springerbriefs in Computer Science) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Fabio Fassetti; Simona E. Rombo; Cristina Serrao

Año

2017

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9783319634760

N° edición

Categorías

Ciencias de la computación

Discriminative Pattern Discovery on Biological Networks (Springerbriefs in Computer Science) (en Inglés)

Fabio Fassetti; Simona E. Rombo; Cristina Serrao (Autor) · Springer · Tapa Blanda

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Reseña del libro "Discriminative Pattern Discovery on Biological Networks (Springerbriefs in Computer Science) (en Inglés)"

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.