Understanding Nature Through the Symbiosis of Information Science, Bioinformatics, and Neuroinformatics

Abstract

This chapter presents some background information, methods, and techniques of information science, bio- and neuroinformatics in their symbiosis. It explains the rationale, motivation, and structure of the Handbook that reflects on this symbiosis. For this chapter, some text and figures from [1.1] have been used. As the introductory chapter, it gives a brief overview of the topics covered in this Springer Handbook of Bio-/Neuroinformatics with emphasis on the symbiosis of the three areas of science concerned: information science (informatics) (IS), bioinformatics (BI), and neuroinformatics (NI). The topics presented and included in this Handbook provide a far from exhaustive coverage of these three areas, but they clearly show that we can better understand nature only if we utilize the methods of IS, BI, and NI, considering their integration and interaction.

Abbreviations

3-D

three-dimensional

ANN

artificial neural network

BI

bioinformatics

DNA

deoxyribonucleic acid

EC

evolutionary computation

EEG

electroencephalography

GA

genetic algorithm

HMM

hidden Markov model

IF

initiation factor

IS

information science

LDA

linear discriminant analysis

MEG

magnetoencephalography

MLR

multiple linear regression

NDEI

nondimensional error index

NI

neuroinformatics

PCA

principle component analysis

RMSE

root mean squared error

RNA

ribonucleic acid

SNR

signal-to-noise ratio

SVM

support vector machine

fMRI

functional magnetic resonance imaging

log

logistic regression

mRNA

messenger RNA

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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.KEDRI – Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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