Clustering Challenges in Biological Networks PDF Download
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Author: Sergiy Butenko
Publisher: World Scientific
ISBN: 9812771662
Category : Science
Languages : en
Pages : 347
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Book Description
This text offers introductory knowledge of a wide range of clustering and other quantitative techniques used to solve biological problems.
Author: Sergiy Butenko
Publisher: World Scientific
ISBN: 9812771662
Category : Science
Languages : en
Pages : 347
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Book Description
This text offers introductory knowledge of a wide range of clustering and other quantitative techniques used to solve biological problems.
Author: Sergiy Butenko
Publisher: World Scientific
ISBN: 9812771654
Category : Science
Languages : en
Pages : 347
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Book Description
This text offers introductory knowledge of a wide range of clustering and other quantitative techniques used to solve biological problems.
Author: Patrick Doreian
Publisher: John Wiley & Sons
ISBN: 1119224705
Category : Mathematics
Languages : en
Pages : 425
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Book Description
Provides an overview of the developments and advances in the field of network clustering and blockmodeling over the last 10 years This book offers an integrated treatment of network clustering and blockmodeling, covering all of the newest approaches and methods that have been developed over the last decade. Presented in a comprehensive manner, it offers the foundations for understanding network structures and processes, and features a wide variety of new techniques addressing issues that occur during the partitioning of networks across multiple disciplines such as community detection, blockmodeling of valued networks, role assignment, and stochastic blockmodeling. Written by a team of international experts in the field, Advances in Network Clustering and Blockmodeling offers a plethora of diverse perspectives covering topics such as: bibliometric analyses of the network clustering literature; clustering approaches to networks; label propagation for clustering; and treating missing network data before partitioning. It also examines the partitioning of signed networks, multimode networks, and linked networks. A chapter on structured networks and coarsegrained descriptions is presented, along with another on scientific coauthorship networks. The book finishes with a section covering conclusions and directions for future work. In addition, the editors provide numerous tables, figures, case studies, examples, datasets, and more. Offers a clear and insightful look at the state of the art in network clustering and blockmodeling Provides an excellent mix of mathematical rigor and practical application in a comprehensive manner Presents a suite of new methods, procedures, algorithms for partitioning networks, as well as new techniques for visualizing matrix arrays Features numerous examples throughout, enabling readers to gain a better understanding of research methods and to conduct their own research effectively Written by leading contributors in the field of spatial networks analysis Advances in Network Clustering and Blockmodeling is an ideal book for graduate and undergraduate students taking courses on network analysis or working with networks using real data. It will also benefit researchers and practitioners interested in network analysis.
Author: Sourav S. Bhowmick
Publisher: Springer
ISBN: 331954621X
Category : Computers
Languages : en
Pages : 159
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Book Description
This book focuses on the data mining, systems biology, and bioinformatics computational methods that can be used to summarize biological networks. Specifically, it discusses an array of techniques related to biological network clustering, network summarization, and differential network analysis which enable readers to uncover the functional and topological organization hidden in a large biological network. The authors also examine crucial open research problems in this arena. Academics, researchers, and advanced-level students will find this book to be a comprehensive and exceptional resource for understanding computational techniques and their applications for a summary of biological networks.
Author: Björn H. Junker
Publisher: John Wiley & Sons
ISBN: 1118209915
Category : Computers
Languages : en
Pages : 278
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Book Description
An introduction to biological networks and methods for their analysis Analysis of Biological Networks is the first book of its kind to provide readers with a comprehensive introduction to the structural analysis of biological networks at the interface of biology and computer science. The book begins with a brief overview of biological networks and graph theory/graph algorithms and goes on to explore: global network properties, network centralities, network motifs, network clustering, Petri nets, signal transduction and gene regulation networks, protein interaction networks, metabolic networks, phylogenetic networks, ecological networks, and correlation networks. Analysis of Biological Networks is a self-contained introduction to this important research topic, assumes no expert knowledge in computer science or biology, and is accessible to professionals and students alike. Each chapter concludes with a summary of main points and with exercises for readers to test their understanding of the material presented. Additionally, an FTP site with links to author-provided data for the book is available for deeper study. This book is suitable as a resource for researchers in computer science, biology, bioinformatics, advanced biochemistry, and the life sciences, and also serves as an ideal reference text for graduate-level courses in bioinformatics and biological research.
Author: Ying Tan
Publisher: Springer
ISBN: 3319409735
Category : Computers
Languages : en
Pages : 564
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Book Description
The LNCS volume LNCS 9714 constitutes the refereed proceedings of the International Conference on Data Mining and Big Data, DMBD 2016, held in Bali, Indonesia, in June 2016. The 57 papers presented in this volume were carefully reviewed and selected from 115 submissions. The theme of DMBD 2016 is "Serving Life with Data Science". Data mining refers to the activity of going through big data sets to look for relevant or pertinent information.The papers are organized in 10 cohesive sections covering all major topics of the research and development of data mining and big data and one Workshop on Computational Aspects of Pattern Recognition and Computer Vision.
Author: Yang Xiang
Publisher: Springer
ISBN: 3642246699
Category : Computers
Languages : en
Pages : 431
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Book Description
This two volume set LNCS 7016 and LNCS 7017 constitutes the refereed proceedings of the 11th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2011, held in Melbourne, Australia, in October 2011. The second volume includes 37 papers from one symposium and three workshops held together with ICA3PP 2011 main conference. These are 16 papers from the 2011 International Symposium on Advances of Distributed Computing and Networking (ADCN 2011), 10 papers of the 4th IEEE International Workshop on Internet and Distributed Computing Systems (IDCS 2011), 7 papers belonging to the III International Workshop on Multicore and Multithreaded Architectures and Algorithms (M2A2 2011), as well as 4 papers of the 1st IEEE International Workshop on Parallel Architectures for Bioinformatics Systems (HardBio 2011).
Author: Matthias Dehmer
Publisher: John Wiley & Sons
ISBN: 111834698X
Category : Mathematics
Languages : en
Pages : 269
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Book Description
Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networks An introduction to complex networks—measures, statistical properties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Density-based enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.
Author: Shinichi Morishita
Publisher: World Scientific
ISBN: 1848165633
Category : Bioinformatics
Languages : en
Pages : 243
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Book Description
This volume contains papers presented at the 20th International Conference on Genome Informatics (GIW 2009) held at the Pacifico Yokohama, Japan from December 14 to 16, 2009. The GIW Series provides an international forum for the presentation and discussion of original research papers on all aspects of bioinformatics, computational biology and systems biology. Its scope includes biological sequence analysis, protein structure prediction, genetic regulatory networks, bioinformatic algorithms, comparative genomics, and biomolecular data integration and analysis. Boasting a history of 20 years, GIW is the longest-running international bioinformatics conference. A total of 18 contributed papers were selected for presentation at GIW 2009 and for inclusion in this book. In addition, this book contains abstracts from the five invited speakers: Sean Eddy (HHMI's Janelia Farm, USA), Minoru Kanehisa (Kyoto University, Japan), Sang Yup Lee (KAIST, Korea), Hideyuki Okano (Keio University, Japan) and Mark Ragan (University of Queensland, Australia).
Author: Pradipta Maji
Publisher: Springer Science & Business Media
ISBN: 3319056301
Category : Computers
Languages : en
Pages : 316
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Book Description
This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The text reviews both established and cutting-edge research, providing a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics. Features: integrates different soft computing and machine learning methodologies with pattern recognition tasks; discusses in detail the integration of different techniques for handling uncertainties in decision-making and efficiently mining large biological datasets; presents a particular emphasis on real-life applications, such as microarray expression datasets and magnetic resonance images; includes numerous examples and experimental results to support the theoretical concepts described; concludes each chapter with directions for future research and a comprehensive bibliography.
