[PDF] Pattern Mining Across Massive Biological Networks For Functional Discovery eBook

Author : Fabio Fassetti
Publisher : Springer
Page : 51 pages
File Size : 24,70 MB
Release : 2017-09-01
Category : Computers
ISBN : 3319634771

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This work provides a review of biological networks as a model for analysis, presenting and discussing a number of illuminating analyses. Biological networks are an effective model for providing insights about biological mechanisms. Networks with different characteristics are employed for representing different scenarios. This powerful model allows analysts to perform many kinds of analyses which can be mined to provide interesting information about underlying biological behaviors. The text also covers techniques for discovering exceptional patterns, such as a pattern accounting for local similarities and also collaborative effects involving interactions between multiple actors (for example genes). Among these exceptional patterns, of particular interest are discriminative patterns, namely those which are able to discriminate between two input populations (for example healthy/unhealthy samples). In addition, the work includes a discussion on the most recent proposal on discovering discriminative patterns, in which there is a labeled network for each sample, resulting in a database of networks representing a sample set. This enables the analyst to achieve a much finer analysis than with traditional techniques, which are only able to consider an aggregated network of each population.

Author : Hiroshi Mamitsuka
Publisher : Humana
Page : 243 pages
File Size : 47,10 MB
Release : 2019-08-04
Category : Science
ISBN : 9781493993260

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This fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences. The first half of the volume examines genomics, particularly metagenomics and epigenomics, which promise to deepen our knowledge of genes and genomes, while the second half of the book emphasizes metabolism and the metabolome as well as relevant medicine-oriented subjects. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that is useful for getting optimal results. Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols, Second Edition serves as an ideal resource for researchers of biology and relevant fields, such as medical, pharmaceutical, and agricultural sciences, as well as for the scientists and engineers who are working on developing data-driven techniques, such as databases, data sciences, data mining, visualization systems, and machine learning or artificial intelligence that now are central to the paradigm-altering discoveries being made with a higher frequency.

Author : Mourad Elloumi
Publisher : John Wiley & Sons
Page : 1192 pages
File Size : 19,38 MB
Release : 2013-12-24
Category : Computers
ISBN : 1118617118

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The first comprehensive overview of preprocessing, mining,and postprocessing of biological data Molecular biology is undergoing exponential growth in both thevolume and complexity of biological data—and knowledgediscovery offers the capacity to automate complex search and dataanalysis tasks. This book presents a vast overview of the mostrecent developments on techniques and approaches in the field ofbiological knowledge discovery and data mining (KDD)—providingin-depth fundamental and technical field information on the mostimportant topics encountered. Written by top experts, Biological Knowledge DiscoveryHandbook: Preprocessing, Mining, and Postprocessing of BiologicalData covers the three main phases of knowledge discovery (datapreprocessing, data processing—also known as datamining—and data postprocessing) and analyzes both verificationsystems and discovery systems. BIOLOGICAL DATA PREPROCESSING Part A: Biological Data Management Part B: Biological Data Modeling Part C: Biological Feature Extraction Part D Biological Feature Selection BIOLOGICAL DATA MINING Part E: Regression Analysis of Biological Data Part F Biological Data Clustering Part G: Biological Data Classification Part H: Association Rules Learning from Biological Data Part I: Text Mining and Application to Biological Data Part J: High-Performance Computing for Biological DataMining Combining sound theory with practical applications in molecularbiology, Biological Knowledge Discovery Handbook is idealfor courses in bioinformatics and biological KDD as well as forpractitioners and professional researchers in computer science,life science, and mathematics.

Author : Poncelet, Pascal
Publisher : IGI Global
Page : 324 pages
File Size : 38,97 MB
Release : 2007-08-31
Category : Computers
ISBN : 1599041642

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"This book provides an overall view of recent solutions for mining, and explores new patterns,offering theoretical frameworks and presenting challenges and possible solutions concerning pattern extractions, emphasizing research techniques and real-world applications. It portrays research applications in data models, methodologies for mining patterns, multi-relational and multidimensional pattern mining, fuzzy data mining, data streaming and incremental mining"--Provided by publisher.

Author : Sourav S. Bhowmick
Publisher : Springer
Page : 159 pages
File Size : 14,24 MB
Release : 2017-04-17
Category : Computers
ISBN : 331954621X

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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 : Charu C. Aggarwal
Publisher : Springer Science & Business Media
Page : 623 pages
File Size : 20,48 MB
Release : 2010-02-02
Category : Computers
ISBN : 1441960457

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Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by well known researchers in the field, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science and engineering.

Author :
Publisher :
Page : pages
File Size : 19,39 MB
Release : 2004
Category :
ISBN :

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As we are moving into the post-genomic era, various high-throughput experimental techniques have been developed to characterize biological systems at the genome scale. The high-throughput data are becoming fundamentally important resources to shed new insights on system-level understanding of the 'organization' and 'dynamics' of molecules (e.g. genes and proteins), relationships between them, interaction cascades, pathways, modules and various networks (i.e. regulation, co-expression and metabolism). This dissertation focuses on developing computational tools to facilitate the process of translating the ever-growing volumes of high-throughput data into significant biological knowledge on protein functions, pathways and modules. Although high-throughput data provide a global picture of biological systems about the underlying mechanisms, the details are often noisy. Integration of heterogeneous data that characterize cellular systems from different aspects (i.e. gene expression and protein-protein interactions) can lead to the comprehensive and coherent discoveries of biological insights. We developed a Bayesian probability framework to predict function for unannotated proteins in yeast through integrating protein binary interaction data, protein complex data and microarray gene expression data. We also extended the computational framework to infer biological pathway in an automated and systematical fashion. Besides bottom-up approaches moving from protein functions to pathways, we also applied top-down approaches to model cellular networks, that is, we started from the architecture of a cellular network to identify functional modules. We applied the k-core algorithm to decompose protein interaction and microarray gene co-expression networks, which provides strong support for modularity principles of networks' structure and function. Dynamic functional modules and protein complexes have been identified by clustering the network constructed from multiple sources of high-throughput data, shedding insights into understanding the organization and dynamics of a living cell. We also proposed a consensus approach to model biological pathway by combining different computational tools and integrating multiple sources of high-throughput data. In the future, with the explosion in the quantity and diversity of high-throughput data, it is vital to develop methodologies and innovative tools in bioinformatics to model biological systems and explore biological knowledge in an iterative fashion.

Author : Sumeet Dua
Publisher : CRC Press
Page : 351 pages
File Size : 47,34 MB
Release : 2012-11-06
Category : Computers
ISBN : 0849328012

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Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overview of the application domains of data mining for bioinformatics to help readers from both biology and computer science backgrounds gain an enhanced understanding of this cross-disciplinary field. The book offers authoritative coverage of data mining techniques, technologies, and frameworks used for storing, analyzing, and extracting knowledge from large databases in the bioinformatics domains, including genomics and proteomics. It begins by describing the evolution of bioinformatics and highlighting the challenges that can be addressed using data mining techniques. Introducing the various data mining techniques that can be employed in biological databases, the text is organized into four sections: Supplies a complete overview of the evolution of the field and its intersection with computational learning Describes the role of data mining in analyzing large biological databases—explaining the breath of the various feature selection and feature extraction techniques that data mining has to offer Focuses on concepts of unsupervised learning using clustering techniques and its application to large biological data Covers supervised learning using classification techniques most commonly used in bioinformatics—addressing the need for validation and benchmarking of inferences derived using either clustering or classification The book describes the various biological databases prominently referred to in bioinformatics and includes a detailed list of the applications of advanced clustering algorithms used in bioinformatics. Highlighting the challenges encountered during the application of classification on biological databases, it considers systems of both single and ensemble classifiers and shares effort-saving tips for model selection and performance estimation strategies.