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Author: Tejaswini Narayanan
Publisher:
ISBN: 9781303251108
Category :
Languages : en
Pages : 105
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Book Description
Community Detection is an interesting computational technique for the analysis of networks. This technique can yield useful insights into the structural organization of a network, and can serve as a basis for understanding the correspondence between structure and function (specific to the domain of the network). In this dissertation, I have sought to leverage this technique for the study of biological networks of practical relevance and significance. The study begins with an exploration of existing techniques for Community Detection, following which an optimization is proposed for one of the widely used graph-theoretic approaches. As the next step, an investigation is performed on the suitability of a machine-learning based algorithm for Community Detection in the context of biological networks. Subsequently, the use of Community Detection for understanding pathology with a specific focus on Duchenne Muscular Dystrophy (DMD), is explored. This illustrated key distinguishing features in the structural and functional organization of the constituent biological pathways as it relates to DMD. Finally, a novel algorithm for Community Detection is proposed, which is motivated by a physical systems analogy. An analysis of the algorithm's properties, together with its applications to biological networks, is also presented. I believe that the techniques and algorithms developed as part of this dissertation in the context of biological networks, have the potential to open up new vistas for therapeutic applications such as targeted drug development.
Author: Tejaswini Narayanan
Publisher:
ISBN: 9781303251108
Category :
Languages : en
Pages : 105
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Book Description
Community Detection is an interesting computational technique for the analysis of networks. This technique can yield useful insights into the structural organization of a network, and can serve as a basis for understanding the correspondence between structure and function (specific to the domain of the network). In this dissertation, I have sought to leverage this technique for the study of biological networks of practical relevance and significance. The study begins with an exploration of existing techniques for Community Detection, following which an optimization is proposed for one of the widely used graph-theoretic approaches. As the next step, an investigation is performed on the suitability of a machine-learning based algorithm for Community Detection in the context of biological networks. Subsequently, the use of Community Detection for understanding pathology with a specific focus on Duchenne Muscular Dystrophy (DMD), is explored. This illustrated key distinguishing features in the structural and functional organization of the constituent biological pathways as it relates to DMD. Finally, a novel algorithm for Community Detection is proposed, which is motivated by a physical systems analogy. An analysis of the algorithm's properties, together with its applications to biological networks, is also presented. I believe that the techniques and algorithms developed as part of this dissertation in the context of biological networks, have the potential to open up new vistas for therapeutic applications such as targeted drug development.
Author: João Gama
Publisher: Springer
ISBN: 3642047475
Category : Computers
Languages : en
Pages : 487
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Book Description
This book constitutes the refereed proceedings of the twelfth International Conference, on Discovery Science, DS 2009, held in Porto, Portugal, in October 2009. The 35 revised full papers presented were carefully selected from 92 papers. The scope of the conference includes the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, as well as their applications.
Author: Günter Rudolph
Publisher: Springer Science & Business Media
ISBN: 3540876995
Category : Computers
Languages : en
Pages : 1183
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Book Description
This book constitutes the refereed proceedings of the 10th International Conference on Parallel Problem Solving from Nature, PPSN 2008, held in Dortmund, Germany, in September 2008. The 114 revised full papers presented were carefully reviewed and selected from 206 submissions. The conference covers a wide range of topics, such as evolutionary computation, quantum computation, molecular computation, neural computation, artificial life, swarm intelligence, artificial ant systems, artificial immune systems, self-organizing systems, emergent behaviors, and applications to real-world problems. The paper are organized in topical sections on formal theory, new techniques, experimental analysis, multiobjective optimization, hybrid methods, and applications.
Author: Narsis A. Kiani
Publisher: Cambridge University Press
ISBN: 1108428878
Category : Computers
Languages : en
Pages : 215
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Book Description
Introduces network inspired approaches for the analysis and integration of large, heterogeneous data sets in the life sciences.
Author: Pietro Hiram Guzzi
Publisher: Elsevier
ISBN: 0128193514
Category : Science
Languages : en
Pages : 212
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Book Description
Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes. The book's authors discuss various graph theoretic and data analytics approaches used to analyze these networks with respect to available tools, technologies, standards, algorithms and databases for generating, representing and analyzing graphical data. As a wide variety of algorithms have been developed to analyze and compare networks, this book is a timely resource. Presents recent advances in biological network analysis, combining Graph Theory, Graph Analysis, and various network models Discusses three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN) and Human Brain Connectomes Includes a discussion of various graph theoretic and data analytics approaches
Author: Ravindra K. Ahuja
Publisher:
ISBN: 9781292042701
Category : Mathematical optimization
Languages : en
Pages : 864
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Book Description
Bringing together the classic and the contemporary aspects of the field, this comprehensive introduction to network flows provides an integrative view of theory, algorithms, and applications. It offers in-depth and self-contained treatments of shortest path, maximum flow, and minimum cost flow problems, including a description of new and novel polynomial-time algorithms for these core models. For professionals working with network flows, optimization, and network programming.
Author: Tejas Puranik
Publisher:
ISBN:
Category :
Languages : en
Pages : 117
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Book Description
Most social networks are characterized by the presence of community structure, viz. the existence of clusters of nodes with a much higher proportion of links within the clusters than between the clusters. Community detection has many applications in many kinds of networks, including social networks and biological networks. Many different approaches have been proposed to solve the problem. An approach that has been shown to scale well to large networks is the Louvain method, based on maximizing modularity, which is a quality function of a partition of the nodes.In this thesis, we address the problem of community detection in evolving social networks. As social networks evolve, the community structure of the network can change. How can the community structure be updated in an efficient way? How often should community structure be updated? In this thesis, we give two methods based on the Louvain algorithm, to determine when to update the community structure. The first method, called the Edge-Distribution-Analysis algorithm, analyzes the newly added edges in order to make this decision. The second method, called the Modularity-Change-Rate algorithm, finds the rate of modularity change in a given network, and uses it to predict whether an update is required.Due to the sparsity of real datasets of evolving networks, we propose three models to generate evolving networks: a Random model, a model based on the well-known phenomenon of homophily in social networks, and another based on the phenomenon of triadic and cyclic closure. Starting with real-world data sets, we used these models to generate evolving networks. We evaluated the Edge-Distribution-Analysis algorithm and Modularity-Change-Rate algorithm on these data sets. Our results show that both our methods predict quite well when the community structure should be updated. They result in significant computational savings compared to approaches that would update the community structure after a fixed number of edge additions, while ensuring that the quality of the community structure is comparable.
Author: Zachary Boyd
Publisher:
ISBN:
Category :
Languages : en
Pages : 104
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Book Description
In recent years, a massive expansion in the amount of available network data in fields such as social networks, food networks in ecology, similarity networks in machine learning, transportation networks, brain networks, and many others has motivated the development of "network science'' to describe all this data. One of the fundamental branches in network science is "community detection,'' or the decomposition of large networks into coherent subnetworks, which is useful for visualization, data exploration, hypothesis formation, approximation of network dynamics, link prediction, and a host of other tasks. Two of the most well-known frameworks for community detection are modularity optimization and stochastic block modeling. They can often uncover meaningful community structure in networks from diverse applications. However, both of these approaches are computationally demanding. In this dissertation, I will show how these two statistically-motivated frameworks for community detection can be reinterpreted more geometrically using the language of graph total variation (TV) and (discretized) surface tension, respectively. This change in perspective allows one to leverage algorithms and analytical tools developed for other problems in the fields of compressed sensing, materials science, and nonlinear partial differential equations. One also can adapt arguments from other domains to obtain theoretical guarantees on the performance of these algorithms. I illustrate these approaches on a number of synthetic and real-world datasets, yielding results competitive with other state-of-the-art techniques on problems from machine learning, image processing, social networks, and biological networks.
Author: Emanuele Cozzo
Publisher: Springer
ISBN: 3319922556
Category : Science
Languages : en
Pages : 124
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Book Description
This book provides the basis of a formal language and explores its possibilities in the characterization of multiplex networks. Armed with the formalism developed, the authors define structural metrics for multiplex networks. A methodology to generalize monoplex structural metrics to multiplex networks is also presented so that the reader will be able to generalize other metrics of interest in a systematic way. Therefore, this book will serve as a guide for the theoretical development of new multiplex metrics. Furthermore, this Brief describes the spectral properties of these networks in relation to concepts from algebraic graph theory and the theory of matrix polynomials. The text is rounded off by analyzing the different structural transitions present in multiplex systems as well as by a brief overview of some representative dynamical processes. Multiplex Networks will appeal to students, researchers, and professionals within the fields of network science, graph theory, and data science.
Author: Geraldo A. Passos
Publisher: Springer
ISBN: 3319119850
Category : Medical
Languages : en
Pages : 348
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Book Description
After sequencing the human genome a decade ago, researchers have continued their projects, but now to try to better understand how, and when, genes are expressed in health and disease. Efforts have been concentrated on the measurement of the expression of RNA transcripts. In an analogy to the genome, the term "transcriptome" was created to refer to the complete set of RNAs in a cell type or tissue in a particular situation. Transcriptomics is the science that studies this issue and it is a branch of functional genomics. Transcriptomics in Heath and Disease provides a comprehensive overview of the science of transcriptomics initially in health, focusing on the concept of the transcriptome and the main methods to evaluate it. The authors discuss the concept and use of gene expression signatures and transcriptional biomarkers in normal development and diseased tissues and organs. As the transcriptome changes depending on the pathology, there is also a focus on the variations in the gene expression in different diseases such as autoimmune, inflammation, cancer and infections. This book should be very useful for researchers in molecular biology focusing on gene expression, human genetics, immunology, and genomics.
