Springer Handbook of Bio-/Neuro-Informatics

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.

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.

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.

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.

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