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Evolving Processes in Time-Space. Deep Learning and Deep Knowledge Representation in Time-Space. Brain-Inspired AI Read chapter Read first chapter

2019 | OriginalPaper | Chapter

17. A Computational Framework for Personalised Modelling. Applications in Bioinformatics

Author : Nikola K. Kasabov

Published in: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Publisher: Springer Berlin Heidelberg

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Abstract

The chapter presents a computational framework for building personalised models (PM) for accurate prediction of an outcome for the individual. First, a general scheme for building PM using integrated feature and model parameter optimisation is presented. The framework is used to develop two specific methods using: (a) traditional ANN techniques; (b) using evolving spiking neural networks (eSNN). Both methods are illustrated on benchmark biomedical data.

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previous chapter Computational Neuro-genetic Modelling
next chapter Personalised Modelling for Integrated Static and Dynamic Data. Applications in Neuroinformatics
Appendix
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Metadata
Title
A Computational Framework for Personalised Modelling. Applications in Bioinformatics
Author
Nikola K. Kasabov
Copyright Year
2019
Publisher
Springer Berlin Heidelberg
Book
Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Print ISBN: 978-3-662-57713-4

Electronic ISBN: 978-3-662-57715-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-662-57715-8_17

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