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Prediction of high centrality nodes from reverse-engineered transcriptional regulator networks
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. Uppsala University, Sweden. (MAM)
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (MAM)ORCID iD: 0000-0002-1624-5147
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2016 (English)In: Proocedings of the 4th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop / [ed] Christos H Skiadas, 2016, p. 517-531Conference paper, Published paper (Refereed)
Abstract [en]

The prioritization of genes based on their centrality in biological networkshas emerged as a promising technique for the prediction of phenotype related genes.A number of methods have been developed to derive one such type of network, i.e.transcriptional regulatory networks, from expression data. In order to reliably prioritizegenes from such networks, it is crucial to investigate how well the inferencemethods reconstruct the centralities that exist in the true biological system. We haverecently reported that the correlation of centrality rankings between reference andinferred networks is only modest when using an unbiased inference approach. In thisstudy we extend on these results and demonstrate that the correlation remains modestalso when using a biased inference utilizing a priori information about transcriptionfactors. However, we show further that despite this lack of a strong correlation, theinferred networks still allow a signicant prediction of genes with high centralities inthe reference networks.

Place, publisher, year, edition, pages
2016. p. 517-531
Keywords [en]
Transcriptional network inference, network inference, graph centrality, degree, betweenness.
National Category
Bioinformatics (Computational Biology) Probability Theory and Statistics
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-36583OAI: oai:DiVA.org:mdh-36583DiVA, id: diva2:1145889
Conference
4th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop
Available from: 2017-09-30 Created: 2017-09-30 Last updated: 2019-02-06Bibliographically approved
In thesis
1. Graph theory based approaches for gene prioritization in biological networks: Application to cancer gene detection in medulloblastoma
Open this publication in new window or tab >>Graph theory based approaches for gene prioritization in biological networks: Application to cancer gene detection in medulloblastoma
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Networks provide an intuitive and highly adaptable means to model relationships between objects. When translated to mathematical graphs, they become further amenable to a plethora of mathematical operations that allow a detailed study of the underlying relational data. Thus, it is not surprising that networks have evolved to a predominant method for analyzing such data in a vast variety of research fields. However, with increasing complexity of the studied problems, application of network modeling also becomes more challenging. Specifically, given a process to be studied, (i) which interactions are important and how can they be modeled, (ii) how can relationships be inferred from complex and potentially noisy data, and (iii) which methods should be used to test hypotheses or answer the relevant questions? This thesis explores the concept and challenges of network analysis in the context of a well-defined application area, i.e. the prediction of cancer genes from biological networks, with an application to medulloblastoma research.

Medulloblastoma represents the most common malignant brain tumor in children. Currently about 70% of treated patients survive, but they often suffer from permanent cognitive sequelae. Medulloblastoma has previously been shown to harbor at least four distinct molecular subgroups. Related studies have also greatly advanced our understanding of the genetic aberrations associated with MB subgroups. However, to translate such findings to novel and improved therapy options, further insights are required into how the dysregulated genes interact with the rest of the cellular system, how such a cross-talk can drive tumor development, and how the arising tumorigenic processes can be targeted by drugs. Establishing such understanding requires investigations that can address biological processes at a more system-wide level, a task that can be approached through the study of cellular systems as mathematical networks of molecular interactions.

This thesis discusses the identification of cancer genes from a network perspective, where specific focus is placed on one particular type of network, i.e. so called gene regulatory networks that model relationships between genes at the expression level. The thesis outlines the bridge between biological and mathematical network concepts. Specifically, the computational challenge of inferring such networks from molecular data is presented. Mathematical approaches for analyzing these networks are outlined and it is explored how such methods might be affected by network inference. Further focus is placed on dealing with the challenges of establishing a suitable gene expression dataset for network inference in MB. Finally, the thesis is concluded with an application of various network approaches in a hypothesis-driven study in MB, in which various novel candidate genes were prioritized.  

Place, publisher, year, edition, pages
Västerås: Mälardalens högskola, 2019
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 286
National Category
Mathematics Bioinformatics (Computational Biology)
Research subject
Mathematics/Applied Mathematics
Identifiers
urn:nbn:se:mdh:diva-42590 (URN)978-91-7485-420-6 (ISBN)
Public defence
2019-03-08, Gamma, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2019-02-06 Created: 2019-02-06 Last updated: 2019-02-14Bibliographically approved

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Engström, ChristopherSilvestrov, Sergei

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