- Title
- PCA based population generation for genetic network optimization
- Creator
- Youseph, Ahammed; Chetty, Madhu; Karmakar, Gour
- Date
- 2018
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/166097
- Identifier
- vital:13412
- Identifier
-
https://doi.org/10.1007/s11571-018-9486-0
- Identifier
- ISBN:1871-4080
- Abstract
- A gene regulatory network (GRN) represents a set of genes and its regulatory interactions. The inference of the regulatory interactions between genes is usually carried out using an appropriate mathematical model and the available gene expression profile. Among the various models proposed for GRN inference, our recently proposed Michaelis–Menten based ODE model provides a good trade-off between the computational complexity and biological relevance. This model, like other known GRN models, also uses an evolutionary algorithm for parameter estimation. Considering various issues associated with such population based stochastic optimization approaches (e.g. diversity, premature convergence due to local optima, accuracy, etc.), it becomes important to seed the initial population with good individuals which are closer to the optimal solution. In this paper, we exploit the inherent strength of principal component analysis (PCA) in a novel manner to initialize the population for GRN optimization. The benefit of the proposed method is validated by reconstructing in silico and in vivo networks of various sizes. For the same level of accuracy, the approach with PCA based initialization shows improved convergence speed.
- Publisher
- Springer Netherlands
- Relation
- Cognitive Neurodynamics Vol. 12, no. 4 (2018), p. 417-429
- Rights
- Copyright © 2018, Springer Science+Business Media B.V., part of Springer Nature.
- Rights
- This metadata is freely available under a CCO license
- Rights
- Open Access
- Subject
- 0606 Physiology; 1116 Medical Physiology; 1702 Cognitive Science; Computational complexity; Gene regulatory network (GRN); Michaelis–Menten kinetics; Principal component analysis (PCA); Segmentation; State space model
- Full Text
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