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Discriminative Pattern Discovery on Biological Networks

  • Fabio Fassetti
  • Simona E. Rombo
  • Cristina Serrao

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-x
  2. Biological Networks

    1. Front Matter
      Pages 1-1
    2. Fabio Fassetti, Simona E. Rombo, Cristina Serrao
      Pages 3-7
    3. Fabio Fassetti, Simona E. Rombo, Cristina Serrao
      Pages 9-20
  3. Pattern Mining

    1. Front Matter
      Pages 21-21
    2. Fabio Fassetti, Simona E. Rombo, Cristina Serrao
      Pages 23-30
    3. Fabio Fassetti, Simona E. Rombo, Cristina Serrao
      Pages 31-45

About this book

Introduction

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.

Keywords

Biological networks Pattern mining Classification systems Gene expression data Phenotype analysis

Authors and affiliations

  1. 1.University of CalabriaCosenzaItaly
  2. 2.University of PalermoPalermoItaly
  3. 3.University of CalabriaCosenzaItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-63477-7
  • Copyright Information The Author(s) 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-63476-0
  • Online ISBN 978-3-319-63477-7
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site
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