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Taking a systematic approach to pattern discovery, this self-contained book supplies sound mathematical definitions and efficient algorithms to explain vital information about biological data. A solid foundation in computational methods, it explores various data patterns, including strings, clusters, permutations, topology, partial orders, and boolean expressions, to capture a different form of regularity in the data. The book focuses on models of biological sequences, including DNA, RNA, and protein sequences. With numerous exercises at the end of each chapter, it also reviews basic statistics, including probability, information theory, and the central limit theorem.

Molecular biology --- Computer. Automation --- Bioinformatics --- Pattern recognition systems --- Computational Biology --- Pattern Recognition, Automated --- methods --- Bioinformatics. --- Pattern recognition systems. --- Pattern Recognition, Automated. --- methods. --- Computational Biology - methods

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Biomathematics. Biometry. Biostatistics --- Information systems --- Bioinformatics. --- Bio-informatique. --- Computational Biology. --- Bio-informatica. --- Bioinformatique. --- Bioinformatique

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"The nervous system is made up of a large number of interacting elements. To understand how such a complex system functions requires the construction and analysis of computational models at many different levels. This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signalling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modelling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience"-- "This book is about how to construct and use computational models of specific parts of the nervous system, such as a neuron, a part of a neuron or a network of neurons. It is designed to be read by people from a wide range of backgrounds from the biological, physical and computational sciences. The word 'model' can mean different things in different disciplines, and even researchers in the same field may disagree on the nuances of its meaning. For example, to biologists, the term 'model' can mean 'animal model'; to physicists, the standard model is a step towards a complete theory of fundamental particles and interactions. We therefore start this chapter by attempting to clarify what we mean by computational models and modelling in the context of neuroscience. Before giving a brief chapter-by-chapter overview of the book, we also discuss what might be called the philosophy of modelling: general issues in computational modelling that recur throughout the book"--

Physiology of nerves and sense organs --- Computer. Automation --- Computational neuroscience. --- Computational neurosciences --- Computational biology --- Neurosciences

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Biomathematics. Biometry. Biostatistics --- Biological techniques --- Molecular biology --- Bioinformatics. --- Biomedical engineering --- Medicine --- Biomedical Engineering. --- Computational Biology --- Data processing. --- methods

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Suitable for advanced undergraduates and postgraduates, "Understanding Bioinformatics" provides a definitive guide to this vibrant and evolving discipline. This book takes a conceptual approach. It guides the reader from first principles through to an understanding of the computational techniques and the key algorithms. "Understanding Bioinformatics" is an invaluable companion for students from their first encounter with the subject through to more advanced studies. This book is divided into seven parts, with the opening part introducing the basics of nucleic acids, proteins and databases. Subsequent parts are divided into 'Applications' and 'Theory' Chapters, allowing readers to focus their attention effectively.In each section, the Applications Chapter provides a fast and straightforward route to understanding the main concepts and 'getting started'. Each of these is then followed by Theory Chapters which give greater detail and present the underlying mathematics. In Part 2, Sequence Alignments, the Applications Chapter shows the reader how to get started on producing and analyzing sequence alignments, and using sequences for database searching, while the next two chapters look closely at the more advanced techniques and the mathematical algorithms involved. The Part 3 covers evolutionary processes and shows how bioinformatics can be used to help build phylogenetic trees. Part 4 looks at the characteristics of whole genomes. In Parts 5 and 6 the focus turns to secondary and tertiary structure - predicting structural conformation and analysing structure-function relationships. The last part surveys methods of analyzing data from a set of genes or proteins of an organism and is rounded off with an overview of systems biology.The writing style of "Understanding Bioinformatics" is notable for its clarity, while the extensive, full-color artwork has been designed to present the key concepts with simplicity and consistency. Each chapter uses mind-maps and flow diagrams to Give an overview of the conceptual links within each topic.

Biomathematics. Biometry. Biostatistics --- Computational Biology --- Bioinformatics --- Bio-informatique --- methods --- Bioinformatics. --- methods. --- Bioinformatica --- 57.087 --- Methods and techniques for parameter estimation. Recording of biological data --- Bioinformatica. --- 57.087 Methods and techniques for parameter estimation. Recording of biological data --- Methods. --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Data processing