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Graph Centrality Based Prediction of Cancer Genes
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Sweden. (MAM)
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Sweden.
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (MAM)ORCID iD: 0000-0002-1624-5147
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Sweden.
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2016 (English)In: Engineering Mathematics II: Algebraic, Stochastic and Analysis Structures for Networks, Data Classification and Optimization / [ed] Sergei Silvestrov; Milica Rancic, Springer, 2016, p. 275-311Chapter in book (Refereed)
Abstract [en]

Current cancer therapies including surgery, radiotherapy and chemotherapy are often plagued by high failure rates. Designing more targeted and personalized treatment strategies requires a detailed understanding of druggable tumor drivergenes. As a consequence, the detection of cancer driver genes has evolved to a critical scientific field integrating both high-through put experimental screens as well as computational and statistical strategies. Among such approaches, network based prediction tools have recently been accentuated and received major focus due to their potential to model various aspects of the role of cancer genes in a biological system. In this chapter, we focus on how graph centralities obtained from biological networks have been used to predict cancer genes. Specifically, we start by discussing the current problems in cancer therapy and the reasoning behind using network based cancer gene prediction, followed by an outline of biological networks, their generation and properties. Finally, we review major concepts, recent results as well as future challenges regarding the use of graph centralities in cancer gene prediction.

Place, publisher, year, edition, pages
Springer, 2016. p. 275-311
Series
Springer Proceedings in Mathematics and Statistics, ISSN 2194-1009 ; 179
Keywords [en]
graph, graph centrality, biological networks, cancer therapies, cancer driver genes, biological system
National Category
Computational Mathematics Bioinformatics (Computational Biology) Bioinformatics and Systems Biology Genetics Medical and Health Sciences
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-33381Scopus ID: 2-s2.0-85012877104ISBN: 978-3-319-42104-9 (print)ISBN: 978-3-319-42105-6 (print)OAI: oai:DiVA.org:mdh-33381DiVA, id: diva2:1034024
Available from: 2016-10-11 Created: 2016-10-11 Last updated: 2019-02-06Bibliographically 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
2. 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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