Biological Pattern Discovery With R: Machine Learning Approaches
Biological Pattern Discovery With R: Machine Learning Approaches

Author: Zheng Rong Yang

Publisher: World Scientific

Published: 2021-09-17

Total Pages: 462

ISBN-13: 9811240132

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This book provides the research directions for new or junior researchers who are going to use machine learning approaches for biological pattern discovery. The book was written based on the research experience of the author's several research projects in collaboration with biologists worldwide. The chapters are organised to address individual biological pattern discovery problems. For each subject, the research methodologies and the machine learning algorithms which can be employed are introduced and compared. Importantly, each chapter was written with the aim to help the readers to transfer their knowledge in theory to practical implementation smoothly. Therefore, the R programming environment was used for each subject in the chapters. The author hopes that this book can inspire new or junior researchers' interest in biological pattern discovery using machine learning algorithms.

Discriminative Pattern Discovery on Biological Networks
Discriminative Pattern Discovery on Biological Networks

Author: Fabio Fassetti

Publisher: Springer

Published: 2017-09-01

Total Pages: 51

ISBN-13: 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.

Pattern Recognition in Computational Molecular Biology
Pattern Recognition in Computational Molecular Biology

Author: Mourad Elloumi

Publisher: John Wiley & Sons

Published: 2015-11-30

Total Pages: 655

ISBN-13: 1119078857

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A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: Pattern Recognition in Sequences, Pattern Recognition in Secondary Structures, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays, Pattern Recognition in Phylogenetic Trees, and Pattern Recognition in Biological Networks. Surveys the development of techniques and approaches on pattern recognition in biomolecular data Discusses pattern recognition in primary, secondary, tertiary and quaternary structures, as well as microarrays, phylogenetic trees and biological networks Includes case studies and examples to further illustrate the concepts discussed in the book Pattern Recognition in Computational Molecular Biology: Techniques and Approaches is a reference for practitioners and professional researches in Computer Science, Life Science, and Mathematics. This book also serves as a supplementary reading for graduate students and young researches interested in Computational Molecular Biology.

Bio-kernel Machines And Applications
Bio-kernel Machines And Applications

Author: Zheng Rong Yang

Publisher: World Scientific

Published: 2024-03-06

Total Pages: 267

ISBN-13: 981128735X

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Due to its capability of handling very complex problems and its high flexibility in adapting to different algorithms, the kernel machine plays a crucial role in machine learning.Bio-Kernel Machines and Applications will introduce a new type of kernel machine for the exploration and modeling between the genotypic inherent structures of short protein sequences or nucleic sequences and the phenotypic biological properties or functions of proteins or nucleotides.The book seeks to establish the fundamentals of the bio-kernel machines by presenting the basic principle and theory of the kernel machine and the various formats of kernel machines, such as string kernel machines adapted for biological applications. The book will also introduce several biological applications of the mutation matrices, demonstrating how mutation matrices can enhance the efficiency and biological relevance of machine learning models applied in specific biological problems.Through analyzing current applications of bio-kernel machines, readers will delve into the advantages of the bio-kernel machines and explore how bio-kernel machines can be further enhanced to tackle a wide spectrum of biological challenges and pave the way for future advancements.

Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R
Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R

Author: Gabriel Valiente

Publisher: CRC Press

Published: 2009-04-08

Total Pages: 370

ISBN-13: 1420069748

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Emphasizing the search for patterns within and between biological sequences, trees, and graphs, Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R shows how combinatorial pattern matching algorithms can solve computational biology problems that arise in the analysis of genomic, transcriptomic, proteomic, metabolomic

Pattern Discovery in Biomolecular Data
Pattern Discovery in Biomolecular Data

Author: Jason T. L. Wang

Publisher: Oxford University Press

Published: 1999-10-28

Total Pages: 272

ISBN-13: 0190283726

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Finding patterns in biomolecular data, particularly in DNA and RNA, is at the center of modern biological research. These data are complex and growing rapidly, so the search for patterns requires increasingly sophisticated computer methods. Pattern Discovery in Biomolecular Data provides a clear, up-to-date summary of the principal techniques. Each chapter is self-contained, and the techniques are drawn from many fields, including graph theory, information theory, statistics, genetic algorithms, computer visualization, and vision. Since pattern searches often benefit from multiple approaches, the book presents methods in their purest form so that readers can best choose the method or combination that fits their needs. The chapters focus on finding patterns in DNA, RNA, and protein sequences, finding patterns in 2D and 3D structures, and choosing system components. This volume will be invaluable for all workers in genomics and genetic analysis, and others whose research requires biocomputing.

Biological Knowledge Discovery Handbook
Biological Knowledge Discovery Handbook

Author: Mourad Elloumi

Publisher: John Wiley & Sons

Published: 2015-02-04

Total Pages: 1126

ISBN-13: 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.

Proceedings of the Fourth SIAM International Conference on Data Mining
Proceedings of the Fourth SIAM International Conference on Data Mining

Author: Michael W. Berry

Publisher: SIAM

Published: 2004-01-01

Total Pages: 556

ISBN-13: 9780898715682

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The Fourth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. This is reflected in the talks by the four keynote speakers who discuss data usability issues in systems for data mining in science and engineering, issues raised by new technologies that generate biological data, ways to find complex structured patterns in linked data, and advances in Bayesian inference techniques. This proceedings includes 61 research papers.

Frequent Pattern Mining
Frequent Pattern Mining

Author: Charu C. Aggarwal

Publisher: Springer

Published: 2014-08-29

Total Pages: 480

ISBN-13: 3319078216

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This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level students in computer science, researchers and practitioners from industry will find this book an invaluable reference.

Pattern Recognition in Bioinformatics
Pattern Recognition in Bioinformatics

Author: Visakan Kadirkamanathan

Publisher: Springer

Published: 2009-08-31

Total Pages: 463

ISBN-13: 3642040314

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This book constitutes the refereed proceedings of the Fourth International Workshop on Pattern Recognition in Bioinformatics, PRIB 2009, held in Sheffield, UK, in September 2009. The 38 revised full papers presented were carefully reviewed and selected from numerous submissions. The topics covered by these papers range from image analysis for biomedical data to systems biology. The conference aims at crating a focus for the development and application of pattern recognition techniques in the biological domain.