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Gene Ontology Analysis of Gene Expression Data Using Hybridized PSO Triclustering

  • 2021
  • OriginalPaper
  • Chapter
Published in:
Authors
N. Narmadha
R. Rathipriya
Published in
Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges
Publisher
Springer International Publishing

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Abstract

The hybridized PSO Triclustering Model is the combination of Binary Particle Swarm Optimization and Simulated Annealing algorithm to extract highly correlated tricluster from the given 3D Gene Expression Dataset. The proposed hybrid Triclustering algorithms namely HPSO- TriC model generally produce higher quality results than standard meta-heuristic triclustering algorithms. Some of the issues in classical meta-heuristic triclustering models can be overcome in the HPSO-TriC model.

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previous chapter Particle Swarm Optimization and Grey Wolf Optimizer to Solve Continuous p-Median Location Problems
next chapter Experimental Studies of Variations Reduction in Chemometric Model Transfer for FT-NIR Miniaturized Sensors
Title
Gene Ontology Analysis of Gene Expression Data Using Hybridized PSO Triclustering
Authors
N. Narmadha
R. Rathipriya
Copyright Year
2021
Book
Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Print ISBN: 978-3-030-59337-7

Electronic ISBN: 978-3-030-59338-4

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-59338-4_22
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