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Loss of conservation of graph centralities in reverse-engineered transcriptional regulatory networks
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University. (Mathematics and Applied Mathematics)
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University.
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (Mathematics and Applied Mathematics)ORCID iD: 0000-0002-1624-5147
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University.
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2015 (English)In: ASMDA 2015 Proceedings: 16th Applied Stochastic Models and Data Analysis International ConferenceWith 4th Demographics 2015 Workshop / [ed] Christos H Skiadas, ISAST: International Society for the Advancement of Science and Technology , 2015, 1077-1091 p.Conference paper, Published paper (Refereed)
Abstract [en]

Graph centralities are often used to prioritize disease genes in transcrip-tional regulatory networks. Studies on small networks of experimentally validatedinteractions emphasize the general validity of this approach and extensions of suchndings have recently also been proposed for networks inferred from expression data.However, due to the noise inherent to expression data, it is largely unknown howwell centralities are preserved in such networks. Specically, while previous stud-ies have evaluated the performance of inference methods on synthetic expression, ithas yet to be established how the choice of method can aect individual centralitiesin the network. Here we compare two centralities between reference networks andnetworks inferred from corresponding simulated expression data using a number ofrelated methods. The results indicate that there exists only a modest conservationof centrality measures for the used inference methods. In conclusion, caution shouldbe exercised when inspecting centralities in reverse-engineered networks and furtherwork will be required to establish the use of such networks for prioritizing genes.

Place, publisher, year, edition, pages
ISAST: International Society for the Advancement of Science and Technology , 2015. 1077-1091 p.
Keyword [en]
Transcriptional network inference, simulated expression, centrality.
National Category
Bioinformatics and Systems Biology Mathematics Computational Mathematics
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-30005ISBN: 978-618-5180-05-8 (print)OAI: oai:DiVA.org:mdh-30005DiVA: diva2:885252
Conference
16th Applied Stochastic Models and Data Analysis International Conference (ASMDA2015) with Demographics 2015 Workshop, 30 June – 4 July 2015, University of Piraeus, Greece
Available from: 2015-12-18 Created: 2015-12-18 Last updated: 2016-10-24Bibliographically approved
In thesis
1. PageRank in Evolving Networks and Applications of Graphs in Natural Language Processing and Biology
Open this publication in new window or tab >>PageRank in Evolving Networks and Applications of Graphs in Natural Language Processing and Biology
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis is dedicated to the use of graph based methods applied to ranking problems on the Web-graph and applications in natural language processing and biology.

Chapter 2-4 of this thesis is about PageRank and its use in the ranking of home pages on the Internet for use in search engines. PageRank is based on the assumption that a web page should be high ranked if it is linked to by many other pages and/or by other important pages. This is modelled as the stationary distribution of a random walk on the Web-graph.

Due to the large size and quick growth of the Internet it is important to be able to calculate this ranking very efficiently. One of the main topics of this thesis is how this can be made more efficiently, mainly by considering specific types of subgraphs and how PageRank can be calculated or updated for those type of graph structures. In particular we will consider the graph partitioned into strongly connected components and how this partitioning can be utilized.

Chapter 5-7 is dedicated to graph based methods and their application to problems in Natural language processing. Specifically given a collection of texts (corpus) we will compare different clustering methods applied to Pharmacovigilance terms (5), graph based models for the identification of semantic relations between biomedical words (6) and modifications of CValue for the annotation of terms in a corpus.

In Chapter 8-9 we look at biological networks and the application of graph centrality measures for the identification of cancer genes. Specifically in (8) we give a review over different centrality measures and their application to finding cancer genes in biological networks and in (9) we look at how well the centrality of vertices in the true network is preserved in networks generated from experimental data.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2016
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 217
National Category
Mathematics
Research subject
Mathematics/Applied Mathematics
Identifiers
urn:nbn:se:mdh:diva-33459 (URN)978-91-7485-298-1 (ISBN)
Public defence
2016-12-08, Kappa, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2016-10-24 Created: 2016-10-24 Last updated: 2016-11-23Bibliographically approved

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Other links

http://www.asmda.es/asmda2015.html

Authority records BETA

Engström, ChristopherSilvestrov, Sergei

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