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Discriminative Pattern Discovery on Biological Networks

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-3-319-63477-7

ISBN: 978-3-319-63476-0

Publisher: Springer International Publishing - Springer Journals

Sep 2, 2017

LitStream

Data Sources and Models

Fassetti, Fabio; Rombo, Simona E.; Serrao, Cristina

Pages: 3 –7

[Biological networks rely on the storage and retrieval of data associated to the physical interactions and/or functional relationships among different actors. In particular, the attention may be on the interactions among cellular components, such as proteins, genes, RNA, or for example on...
LitStream

Problems and Techniques

Fassetti, Fabio; Rombo, Simona E.; Serrao, Cristina

Pages: 9 –20

[When biological networks are considered, the extraction of interesting knowledge often involves subgraphs isomorphism check that is known to be NP-complete. For this reason, many approaches try to simplify the problem under consideration by considering structures simpler than graphs, such as...
LitStream

Exceptional Pattern Discovery

Fassetti, Fabio; Rombo, Simona E.; Serrao, Cristina

Pages: 23 –30

[This chapter is devoted to a discussion on exceptional pattern discovery, namely on scenarios, contexts, and techniques concerning the mining of patterns which are so rare or so frequent to be considered as exceptional and, then, of interest for an expert to shed lights on the domain. Frequent...
LitStream

Discriminating Graph Pattern Mining from Gene Expression Data

Fassetti, Fabio; Rombo, Simona E.; Serrao, Cristina

Pages: 31 –45

[Here we consider the problem of mining gene expression data in order to single out interesting features characterizing healthy/ unhealthy samples of an input dataset. The presented approach is based on a network model of the input gene expression data, where there is a labeled graph for each...

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