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

  • Nikola Kasabov
Part of the Springer Handbooks book series (SHB)


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.


Support Vector Machine Hide Markov Model Linear Discriminant Analysis Bayesian Classifier Spike Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.





artificial neural network




deoxyribonucleic acid


evolutionary computation




genetic algorithm


hidden Markov model


initiation factor


information science


linear discriminant analysis




multiple linear regression


nondimensional error index




principle component analysis


root mean squared error


ribonucleic acid


signal-to-noise ratio


support vector machine


functional magnetic resonance imaging


logistic regression


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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