[PDF] Biological Knowledge Discovery Through Mining Multiple Sources Of High Throughput Data eBook

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Page : pages
File Size : 37,19 MB
Release : 2004
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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 : Mourad Elloumi
Publisher : John Wiley & Sons
Page : 1126 pages
File Size : 20,26 MB
Release : 2015-02-04
Category : Computers
ISBN : 1118853725

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The first comprehensive overview of preprocessing, mining, and postprocessing of biological data Molecular biology is undergoing exponential growth in both the volume and complexity of biological data and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD) providing in-depth fundamental and technical field information on the most important topics encountered. Written by top experts, Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data covers the three main phases of knowledge discovery (data preprocessing, data processing also known as data mining and data postprocessing) and analyzes both verification systems 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 Data Mining Combining sound theory with practical applications in molecular biology, Biological Knowledge Discovery Handbook is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.

Author : Jake Y. Chen
Publisher : CRC Press
Page : 736 pages
File Size : 35,82 MB
Release : 2009-09-01
Category : Computers
ISBN : 1420086855

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Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplin

Author : Bo Song
Publisher :
Page : 0 pages
File Size : 38,47 MB
Release : 2020
Category : Data mining
ISBN :

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The amount of available data has experienced significant growth as the result of technology advances in this era of Big Data. The biomedical domain, in particular, is one exemplar field where the number and scale of data sources have increased exponentially in the last decade. They are expected to keep growing even more rapidly to reach the level of Zetta bytes per year in the following year very soon. While more data obtained from advanced biotechnologies, such as high-throughput sequencing that encodes valuable information, are becoming overwhelmed, to discover knowledge from which for biology and medical research is still facing challenging problems with existing approaches. We study in this dissertation how to effectively and efficiently utilize these large-scare data from different numbers and types of sources for biomedical knowledge discovery. Raw data from biological organism such as microbiome usually have intrinsic high dimensionality of the feature space, which inevitably and exponentially raises the computational complexity of existing algorithms. We proposed a new approach using spectral interpolation technique to represent the high-dimensional data in low dimension space that not only greatly improves the efficiency of computing large-scale data but also preserves as much information as possible from original data. The resulting preferable outcomes for clustering and visualization tasks better facilitate the knowledge revealing of patterns and insights for microbial communities. We further studied how to enhance knowledge discovery while more than one data sources are available. Large-scale relational data such as protein-protein interactions (PPI) can be constructed in the form of network to invoke a system-wide perspective than traditional mechanistic approaches to interpret complex biological processes and functionalities. While bio-experiments are exhausted and costly, with two or more networks from different data sources we can apply computational comparative analysis such as Network Alignment to bridge the knowledge between well-studied species and under-examined species. We proposed new methods to globally align multiple large-scale biological networks from different species at the same time. We utilize both topological features and biological features of PPI networks and search heuristically for the best results. Representation learning for network is also integrated into our proposed framework to provide a new way to quantify the structural features of a node with its surrounding topology for the node embedding. The real data experiments showed promising results in finding homologous proteins as well as conserved protein complexes in poor-studied species for knowledge transferring from well-studied species. Besides utilizing homogeneous data from one and more data sources of one type, we keep exploring the possibility of harnessing sources of different types to take advantage of their underlying relational knowledge across heterogeneous data and capture the complex biomedical associations. The heterogeneous disease information networks we formulated in one research include types of sources from disease, pathway, and chemicals. They are filtered and calculated using Dynamic Time Warping (DTW) algorithm and meta path method for topological and semantics scores which lead to effective measurement of the similarity of diseases. In another study, we proposed a novel framework with Graph Convolutional Network to identify and predict disease-RNAs associations to better support the discovery of relational knowledge at the molecular level for medical applications such as disease diagnosis, therapy, and monitoring.

Author : Rémy D. Hoffmann
Publisher : John Wiley & Sons
Page : 322 pages
File Size : 42,77 MB
Release : 2013-09-25
Category : Medical
ISBN : 3527656006

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Written for drug developers rather than computer scientists, this monograph adopts a systematic approach to mining scientifi c data sources, covering all key steps in rational drug discovery, from compound screening to lead compound selection and personalized medicine. Clearly divided into four sections, the first part discusses the different data sources available, both commercial and non-commercial, while the next section looks at the role and value of data mining in drug discovery. The third part compares the most common applications and strategies for polypharmacology, where data mining can substantially enhance the research effort. The final section of the book is devoted to systems biology approaches for compound testing. Throughout the book, industrial and academic drug discovery strategies are addressed, with contributors coming from both areas, enabling an informed decision on when and which data mining tools to use for one's own drug discovery project.

Author : Wesley W. Chu
Publisher : Springer Science & Business Media
Page : 314 pages
File Size : 37,22 MB
Release : 2013-09-24
Category : Technology & Engineering
ISBN : 3642408370

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The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation. The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.

Author : Sumeet Dua
Publisher : CRC Press
Page : 351 pages
File Size : 28,27 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.

Author : Kang Ning
Publisher : Springer Nature
Page : 173 pages
File Size : 32,20 MB
Release : 2023-01-15
Category : Medical
ISBN : 9811982104

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This book features multi-omics big-data integration and data-mining techniques. In the omics age, paramount of multi-omics data from various sources is the new challenge we are facing, but it also provides clues for several biomedical or clinical applications. This book focuses on data integration and data mining methods for multi-omics research, which explains in detail and with supportive examples the “What”, “Why” and “How” of the topic. The contents are organized into eight chapters, out of which one is for the introduction, followed by four chapters dedicated for omics integration techniques focusing on several omics data resources and data-mining methods, and three chapters dedicated for applications of multi-omics analyses with application being demonstrated by several data mining methods. This book is an attempt to bridge the gap between the biomedical multi-omics big data and the data-mining techniques for the best practice of contemporary bioinformatics and the in-depth insights for the biomedical questions. It would be of interests for the researchers and practitioners who want to conduct the multi-omics studies in cancer, inflammation disease, and microbiome researches.

Author : Gil Alterovitz
Publisher : John Wiley & Sons
Page : 306 pages
File Size : 26,53 MB
Release : 2011-04-20
Category : Medical
ISBN : 1119995833

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There is an increasing need throughout the biomedical sciences for a greater understanding of knowledge-based systems and their application to genomic and proteomic research. This book discusses knowledge-based and statistical approaches, along with applications in bioinformatics and systems biology. The text emphasizes the integration of different methods for analysing and interpreting biomedical data. This, in turn, can lead to breakthrough biomolecular discoveries, with applications in personalized medicine. Key Features: Explores the fundamentals and applications of knowledge-based and statistical approaches in bioinformatics and systems biology. Helps readers to interpret genomic, proteomic, and metabolomic data in understanding complex biological molecules and their interactions. Provides useful guidance on dealing with large datasets in knowledge bases, a common issue in bioinformatics. Written by leading international experts in this field. Students, researchers, and industry professionals with a background in biomedical sciences, mathematics, statistics, or computer science will benefit from this book. It will also be useful for readers worldwide who want to master the application of bioinformatics to real-world situations and understand biological problems that motivate algorithms.

Author : Jason T. L. Wang
Publisher : Springer Science & Business Media
Page : 356 pages
File Size : 25,14 MB
Release : 2005
Category : Computers
ISBN : 9781852336714

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Written especially for computer scientists, all necessary biology is explained. Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.