Discriminative pattern discovery on biological networks

51 Pages·2017·1.204 MB·English

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

SPRINGER BRIEFS IN COMPUTER SCIENCE
Fabio Fassetti
Simona E. Rombo
Cristina Serrao
Discriminative
Pattern Discovery
on Biological
Networks
123
SpringerBriefs in Computer Science
Series editors
Stan Zdonik, Brown University, Providence, Rhode Island, USA
Shashi Shekhar, University of Minnesota, Minneapolis, Minnesota, USA
Xindong Wu, University of Vermont, Burlington, Vermont, USA
LakhmiC.Jain,UniversityofSouthAustralia,Adelaide,SouthAustralia,Australia
David Padua, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
Xuemin (Sherman) Shen, University of Waterloo, Waterloo, Ontario, Canada
Borko Furht, Florida Atlantic University, Boca Raton, Florida, USA
V.S. Subrahmanian, University of Maryland, College Park, Maryland, USA
Martial Hebert, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
Katsushi Ikeuchi, University of Tokyo, Tokyo, Japan
Bruno Siciliano, Università di Napoli Federico II, Napoli, Italy
Sushil Jajodia, George Mason University, Fairfax, Virginia, USA
Newton Lee, Newton Lee Laboratories, LLC, Tujunga, California, USA
More information about this series at http://www.springer.com/series/10028
Fabio Fassetti Simona E. Rombo
(cid:129)
Cristina Serrao
Discriminative Pattern
Discovery on Biological
Networks
123
FabioFassetti Cristina Serrao
University of Calabria University of Calabria
Cosenza Cosenza
Italy Italy
Simona E. Rombo
University of Palermo
Palermo
Italy
ISSN 2191-5768 ISSN 2191-5776 (electronic)
SpringerBriefs inComputer Science
ISBN978-3-319-63476-0 ISBN978-3-319-63477-7 (eBook)
DOI 10.1007/978-3-319-63477-7
LibraryofCongressControlNumber:2017947457
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Preface
Biological networks are an effective model for providing insights about biological
mechanisms. Networkswith different characteristics areemployedfor representing
different scenarios. Thispowerful model allows analysts toperformmany kinds of
analyses which are proved to mine interesting information about underlying bio-
logical behaviors. This work intends to survey on biological networks as a model
forsubsequentanalyseswhicharealsopresentedanddiscussed.Then,itfocuseson
techniquesfordiscoveringexceptionalpatterns,aparticularkindofanalysiswhich
is witnessing a great interest due to the importance of the knowledge it is able to
mine, with a pattern accounting for local similarities and also collaborative effects
involving interactions between multiple actors (for example, genes). Among
exceptional patterns, of particular interest are discriminative ones, namely patterns
which are able to discriminate between two input populations (for example,
healthy/unhealthy samples). The work will also include discussions on the most
recent proposal on discovering discriminative patterns, where there is a labeled
networkforeachsample,resultinginadatabaseofnetworksrepresentingasample
set. Previous techniques are able to just consider an aggregated network of each
population, thus allowing the analyst to get a much more fine analysis. In more
details,edge-labelednetworksareusedandthediscriminativepowerofapatternis
measuredbasedonedgeweights,whicharerepresentativeofhowmuchrelevantis
the co-expression between two genes.
Rende, Italy Fabio Fassetti
Palermo, Italy Simona E. Rombo
April 2017 Cristina Serrao
v
Acknowledgements
This research has been partially supported by the PRIN project 20122F87B2 titled
“Compositional Approaches for the Characterization and Mining of Omics Data”
co-financed by the Italian Ministry of Education, University and Research and
INdAM—GNCS Project 2016: “Approcci integrativi e computazionali per lestra-
zione di conoscenza da reti funzionali”.
vii
Contents
Part I Biological Networks
1 Data Sources and Models. ..... .... .... .... .... .... ..... .... 3
1.1 Protein Data.... .... ..... .... .... .... .... .... ..... .... 3
1.1.1 Protein–Protein Interaction Networks .... .... ..... .... 3
1.2 Disease Data.... .... ..... .... .... .... .... .... ..... .... 4
1.2.1 Genotype–Phenotype Networks. .... .... .... ..... .... 5
1.3 Co-expression Data... ..... .... .... .... .... .... ..... .... 5
1.3.1 Network Populations. .... .... .... .... .... ..... .... 6
References.. .... .... .... ..... .... .... .... .... .... ..... .... 6
2 Problems and Techniques. ..... .... .... .... .... .... ..... .... 9
2.1 Network Alignment .. ..... .... .... .... .... .... ..... .... 9
2.1.1 Techniques ... ..... .... .... .... .... .... ..... .... 10
2.1.2 Querying. .... ..... .... .... .... .... .... ..... .... 12
2.2 Network Clustering... ..... .... .... .... .... .... ..... .... 13
2.2.1 Techniques ... ..... .... .... .... .... .... ..... .... 13
2.3 Network Motif Extraction... .... .... .... .... .... ..... .... 15
2.3.1 Techniques ... ..... .... .... .... .... .... ..... .... 16
References.. .... .... .... ..... .... .... .... .... .... ..... .... 18
Part II Pattern Mining
3 Exceptional Pattern Discovery .. .... .... .... .... .... ..... .... 23
3.1 Frequent Pattern Mining.... .... .... .... .... .... ..... .... 23
3.2 Emerging Patterns and Contrast Sets .. .... .... .... ..... .... 24
3.3 Pattern Discovery on Networks .. .... .... .... .... ..... .... 25
3.4 Pattern Discovery on Biological Data.. .... .... .... ..... .... 26
References.. .... .... .... ..... .... .... .... .... .... ..... .... 28
ix
x Contents
4 Discriminating Graph Pattern Mining
from Gene Expression Data.... .... .... .... .... .... ..... .... 31
4.1 Motivations. .... .... ..... .... .... .... .... .... ..... .... 31
4.2 Network Model . .... ..... .... .... .... .... .... ..... .... 33
4.2.1 Strength of the Relationships .. .... .... .... ..... .... 34
4.2.2 Relevance of the Relationships. .... .... .... ..... .... 35
4.2.3 Building Networks .. .... .... .... .... .... ..... .... 36
4.3 Statement of the Problem... .... .... .... .... .... ..... .... 37
4.3.1 Discriminating Pattern.... .... .... .... .... ..... .... 38
4.3.2 Problem Definition .. .... .... .... .... .... ..... .... 40
4.4 Technique.. .... .... ..... .... .... .... .... .... ..... .... 40
4.4.1 Algorithm .... ..... .... .... .... .... .... ..... .... 40
4.4.2 Strength Computation Details.. .... .... .... ..... .... 41
4.4.3 Relevance Computation Details. .... .... .... ..... .... 43
References.. .... .... .... ..... .... .... .... .... .... ..... .... 45
Part I
Biological Networks