Springer Handbook of Bio-/Neuro-Informatics, 1st Edition

  • Published By:
  • ISBN-10: 3642305741
  • ISBN-13: 9783642305740
  • DDC: 572.80285
  • Grade Level Range: College Freshman - College Senior
  • 1290 Pages | eBook
  • Original Copyright 2014 | Published/Released June 2014
  • This publication's content originally published in print form: 2014

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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.

Table of Contents

Front Cover.
Half Title Page.
Other Frontmatter.
Title Page.
Copyright Page.
About the Editor.
About the Part Editors.
List of Authors.
List of Abbreviations.
1: Understanding Nature Through the Symbiosis of Information Science, Bioinformatics, and Neuroinformatics.
2: Understanding Information Processes in Biological Systems.
3: Information Processing at the Cellular Level: Beyond the Dogma.
4: Dielectrophoresis: Integrated Approaches for Understanding the Cell.
5: Information Processing at the Genomics Level.
6: Understanding Information Processes at the Proteomics Level.
7: Pattern Formation and Animal Morphogenesis.
8: Understanding Evolving Bacterial Colonies.
9: Molecular Biology, Genome and Proteome Informatics.
10: Exploring the Interactions and Structural Organization of Genomes.
11: Detecting MicroRNA Signatures Using Gene Expression Analysis.
12: Bioinformatic Methods to Discover Cis-regulatory Elements in mRNAs.
13: Protein Modeling and Structural Prediction.
14: Machine Learning Methods for the Analysis, Modeling and Knowledge Discovery from Bioinformatics Data.
15: Machine Learning Methodology in Bioinformatics.
16: Case-Based Reasoning for Biomedical Informatics and Medicine.
17: Analysis of Multiple DNA Microarray Datasets.
18: Fuzzy Logic and Rule-Based Methods in Bioinformatics.
19: Phylogenetic Cladograms: Tools for Analyzing Biomedical Data.
20: Protein Folding Recognition.
21: Kernel Methods and Applications in Bioinformatics.
22: Modeling Regulatory Networks: The Systems Biology Approach.
23: Path Finding in Biological Networks.
24: Inferring Transcription Networks from Data.
25: Computational Methods for Analysis of Transcriptional Regulation.
26: Inferring Genetic Networks with a Recurrent Neural Network Model Using Differential Evolution.
27: Structural Pattern Discovery in Protein–Protein Interaction Networks.
28: Molecular Networks – Representation and Analysis.
29: Whole-Exome Sequencing Data – Identifying Somatic Mutations.
30: Bioinformatics Databases and Ontologies.
31: Biological Databases.
32: Ontologies for Bioinformatics.
33: Bioinformatics in Medicine, Health and Ecology.
34: Statistical Signal Processing for Cancer Stem Cell Formation.
35: Epigenetics.
36: Dynamics of Autoimmune Diseases.
37: Nutrigenomics.
38: Bioinformatics and Nanotechnologies: Nanomedicine.
39: Personalized Information Modeling for Personalized Medicine.
40: Health Informatics.
41: Ecological Informatics for the Prediction and Management of Invasive Species.
42: Understanding Information Processes in the Brain and the Nervous System.
43: Information Processing in Synapses.
44: Computational Modeling with Spiking Neural Networks.
45: Statistical Methods for fMRI Activation and Effective Connectivity Studies.
46: Neural Circuit Models and Neuropathological Oscillations.
47: Understanding the Brain via fMRI Classification.
48: Advanced Signal Processing Methods for Brain Signal Analysis and Modeling.
49: Nonlinear Adaptive Filtering in Kernel Spaces.
50: Recurrence Plots and the Analysis of Multiple Spike Trains.
51: Adaptive Multiscale Time-Frequency Analysis.
52: Information Modeling of Perception, Sensation and Cognition.
53: Modeling Vision with the Neocognitron.
54: Information Processing in the Gustatory System.
55: EEG Signal Processing for Brain-Computer Interfaces.
56: Brain-like Information Processing for Spatio-Temporal Pattern Recognition.
57: Neurocompuatational Models of Natural Language.
58: Neuroinformatics Databases and Ontologies.
59: Ontologies and Machine Learning Systems.
60: Integration of Large-Scale Neuroinformatics – The INCF.
61: Information Modeling for Understanding and Curing Brain Diseases.
62: Alzheimer's Disease.
63: Integrating Data for Modeling Biological Complexity.
64: A Machine Learning Pipeline for Identification of Discriminant Pathways.
65: Computational Neurogenetic Modeling: Gene-Dependent Dynamics of Cortex and Idiopathic Epilepsy.
66: Information Methods for Predicting Risk and Outcome of Stroke.
67: sEMG Analysis for Recognition of Rehabilitation Actions.
68: Nature Inspired Integrated Information Technologies.
69: Brain-Like Robotics.
70: Developmental Learning for User Activities.
71: Quantum and Biocomputing – Common Notions and Targets.
72: Brain, Gene, and Quantum Inspired Computational Intelligence.
73: The Brain and Creativity.
74: The Allen Brain Atlas.
About the Authors.
Detailed Contents.
Subject Index.