Overview
- Provides a review of biological networks as a model for analysis, presenting and discussing a number of analyses
- Describes techniques for discovering exceptional patterns, with a focus on discriminative patterns
- Discusses a method for discovering discriminative patterns, which generates a database of networks representing a sample set
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (4 chapters)
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Front Matter
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Biological Networks
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Front Matter
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Pattern Mining
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Front Matter
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Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Discriminative Pattern Discovery on Biological Networks
Authors: Fabio Fassetti, Simona E. Rombo, Cristina Serrao
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-319-63477-7
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2017
Softcover ISBN: 978-3-319-63476-0Published: 11 September 2017
eBook ISBN: 978-3-319-63477-7Published: 01 September 2017
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: X, 45
Number of Illustrations: 4 b/w illustrations
Topics: Computational Biology/Bioinformatics, Pattern Recognition, Data Mining and Knowledge Discovery, Bioinformatics, Gene Expression
Industry Sectors: Biotechnology, IT & Software, Pharma