Springer Handbook of Bio-/Neuro-Informatics 2014
Published on 4. January 2013
Book
Mixed media product
1229 pages
978-3-642-30575-7 (ISBN)
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Description
The Springer Handbook of Bio-/Neuro-Informatics is the first published book in one volume that explains together the basics and the state-of-the-art of two major science disciplines in their interaction and mutual relationship, namely: information sciences, bioinformatics and neuroinformatics. Bioinformatics is the area of science which is concerned with the information processes in biology and the development and applications of methods, tools and systems for storing and processing of biological information thus facilitating new knowledge discovery. Neuroinformatics is the area of science which is concerned with the information processes in biology and the development and applications of methods, tools and systems for storing and processing of biological information thus facilitating new knowledge discovery. The text contains 62 chapters organized in 12 parts, 6 of them covering topics from information science and bioinformatics, and 6 cover topics from information science and neuroinformatics. Each chapter consists of three main sections: introduction to the subject area, presentation of methods and advanced and future developments.
The Springer Handbook of Bio-/Neuroinformatics can be used as both a textbook and as a reference for postgraduate study and advanced research in these areas. The target audience includes students, scientists, and practitioners from the areas of information, biological and neurosciences. With Forewords by Shun-ichi Amari of the Brain Science Institute, RIKEN, Saitama and Karlheinz Meier of the University of Heidelberg, Kirchhoff-Institute of Physics and Co-Director of the Human Brain Project.
The Springer Handbook of Bio-/Neuroinformatics can be used as both a textbook and as a reference for postgraduate study and advanced research in these areas. The target audience includes students, scientists, and practitioners from the areas of information, biological and neurosciences. With Forewords by Shun-ichi Amari of the Brain Science Institute, RIKEN, Saitama and Karlheinz Meier of the University of Heidelberg, Kirchhoff-Institute of Physics and Co-Director of the Human Brain Project.
Reviews / Votes
From the book reviews: "This handbook is structured in 12 parts, covering a wide variety of notions associated with bio- and neuroinformatics ... . The handbook is a valuable and timely resource, which can be used by undergraduates, postgraduates and established researchers. By presenting together a variety of (linked) approaches, it provides a fantastic source for bioinformaticians and neuroinformaticians alike." (Irina Ioana Mohorianu, zbMATH, Vol. 1304, 2015) "This is the very best of the encyclopedias and comprehensive book's about both Medical, Molecular, Genetic, and Neural Informatics currently available. ... this is a good textbook for graduate students as a Primer on Neurophysiology and Molecular Medicine. Professionals in these areas will find this book very useful in their daily work ... . book is very well referenced at the end of each chapter discussion. ... I am pleased to recommend this book to new researchers in the field." (Joseph J. Grenier, Amazon.com, November, 2014)More details
Edition
2014 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Target group
Professional and scholarly
Illustrations
290 black & white illustrations, 200 colour illustrations, 92 colour tables, biography
ISBN-13
978-3-642-30575-7 (9783642305757)
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Nikola Kasabov
Springer Handbook of Bio-/Neuro-Informatics
Book
11/2013
Springer
€349.00
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Person
Nikola K Kasabov is currently the Director of the Knowledge Engineering & Discovery Research Institute and Personal Chair of Knowledge Engineering in the School of Information Technology, Auckland University of Technology, New Zealand. He has published over 350 works, among them journal papers, textbooks, edited research books and monographs, conference papers, book chapters, edited conference proceedings, patents and authorship certificates in the area of intelligent systems, connectionist and hybrid connectionist systems, fuzzy systems, expert systems, speech recognition, bioinformatics, neurocomputing and neural networks. Prof. Kasabov is a Fellow of IEEE, Fellow of the Royal Society of New Zealand and the New Zealand Computer Society, President of the International Neural Network Society (INNS), Past President of the Asia Pacific Neural Network Assembly (APNNA) and also a member of INNS, ENNS, and the IEEE Computer Society. Recently Prof. Kasabov was endowed the title of 'Guest Professor' at Shanghai Jiao Tong University, China. Prof. Kasabov is the General Chairman of a series of biannual international conferences on Neurocomputing in New Zealand. In 2007 Prof. Kasabov was awarded The Bayer Science Innovator Award. He received The Royal Society of New Zealand Silver Medal for contribution to Science and Technology in 2001. He is an Associate Editor of numerous international journals and a leader of a National Research Programme funded by the Foundation for Research, Science and Technology (FRST)
Content
Chap. 1 Understanding Nature - Symbiosis of Information Science, Bioinformatics and Neuroinformatics Part A Understanding Information Processes in Biological Systems (Ed. Heike Sichtig) Chap. 2 Information Processing at the Cellular Level.- Chap. 3 Integrated Approaches for Understadning the Cell.- Chap. 4 Information Processing at the Genomics Level.- Chap. 5 Understanding Information Processes at the Proteomics Level.- Chap. 6 Pattern Formation and Animal Morphogenesis.- Chap. 7 Understanding Evolving Bacteria Colonies Part B Molecular Biology, Genome and Proteome Informatics (Ed.: Chris Brown) Chap. 8 Exploring Interactions and Structural Organization of Genomes.- Chap. 9 Detecting microRNA Signatures Using Gene Expression Analysis.- Chap. 10 Bioinformatics Methods to Discover Cis-Regulatory Elements.- Chap. 11 Protein Modeling and Structural Prediction Part C Machine-Learning Methods (Eds: Irwin King, Kaizhu Huang, Heike Sichtig) Chap. 12 Machine Learning Methodology in Bioinformatics.- Chap. 13 Case-Based Reasoning for Biomedical Informatics and Medicine.- Chap. 14 Analysis of Multiple DNA Microarray Datasets.- Chap. 15 Fuzzy Logic and Rule-Based Methods in Bioinformatics.- Chap. 16 Phylogenetic Cladograms: Understanding and Learning from Biomedical Data.- Chap. 17 Understanding Protein Folding Modeling.- Chap. 18 Kernel Methods and Applications in Bioinformatics Part D Modeling Regulatory Networks: The Systems Biology Approach (Eds: Chris Brown, Heike Sichtig, Irwin King, Kaizhu Huang, Francesco Masulli) Chap. 19 Path Finding in Biological Networks.- Chap. 20 Inferring Transcription Networks from Data.- Chap. 21 Analysis of Transcriptional Regulation.- Chap. 22 Inferring Genetic Networks.- Chap. 23 Structural Pattern Discovery.- Chap. 24 Molecular Networks - Representation and Analysis.- Chap. 25 Whole-Exome Sequencing Data Part E Bioinformatics Databases and Ontologies (Ed.: Francesco Masulli) Chap. 26 Bioinformatics Databases.- Chap. 27 Ontologies for Bioinformatics Part F Bioinformatics in Medicine, Health and Ecology (Eds: Francesco Masulli, Danilo Mandic) Chap. 28 Statistical Signal Processing Models And Methods.- Chap. 29 Epigenetics.- Chap. 30 Control of Autoimmune Diseases.- Chap. 311 Nutrigenomics.- Chap. 32 Bioinformatics and Nanotechnologies: Nanomedicine.- Chap. 33 Information Modeling Technologies for Personalized Medicine.- Chap. 34 Health Informatics.- Chap. 35 Ecological Informatics Part G Understanding Information Processes in the Brain and the Nervous System (Ed.: Heike Sichtig) Chap. 36 Information Processing in Synapses.- Chap. 37 Spiking Neural Networks.- Chap. 38 Statistical Methods for fMRI Activation Detection.- Chap. 39 Neural Circuit Models and Neuropathological Oscillations.- Chap. 40 Understanding the Brain via fMRI Classification Part H Advanced Signal-Processing Methods for Brain Signal Analysis and Modeling (Ed.: Danilo Mandic) Chap. 41 Nonlinear Adaptive Filtering in Kernel Spaces.- Chap. 42 Analysis of Multiple Spike Trains.- Chap. 43 Adaptive Multiscale Time-Frequency Analysis Part I Information Modeling of Perception, Sensation and Cognition (Eds: Lubica Benuskova, Heike Sichtig) Chap. 44 Modeling Vision with the Neocognitron.- Chap. 45 Information Processing in the Gustatory System.- Chap. 46 EEG Signal Processing for Brain Computer Interfaces.- Chap. 47 Spiking Neural Networks.- Chap. 48 Neurocomputational Models of Natural Language Part J Neuroinformatics Databases and Ontologies (Eds: Shiro Usui, Raphael Ritz) Chap. 49 Ontologies and Machine Learning Systems .- Chap. 50 Integration of Large-Scale Neuroinformatics Part K Information Modeling for Brain Diseases (Eds: Lubica Benuskova, Francesco Masulli) Chap. 51 Alzheimer's Disease.- Chap. 52 Integrating Data for Modeling Biological Complexity.- Chap. 53 A Machine Learning Pipeline .- Chap. 54 Modeling Gene-Dependent Dynamics of Cortex and Epilepsy.- Chap. 55 Information Methods for Predicting Stroke.- Chap. 56 Recognition of Rehabilitation Actions Part L Nature Inspired Integrated Information Technologies (Eds: Lubica Benuskova, Danilo Mandic) Chap. 57 Brain-Like Robotics.- Chap. 58 Developmental Learning for User Activities.- Chap. 59 Quantum and Biocomputing - Common Notions and Targets.- Chap. 60 Brain-, Gene-, and Quantum-Inspired Computational Intelligence.- Chap. 61 The Brain and Creativity.- Chap. 62 The Allen Brain Atlas Acknowledgements.- About the Authors.- Subject Index