A Study of Particle Swarm Optimization in Gene Regulatory Networks Inference

2006 ◽  
pp. 648-653 ◽  
Author(s):  
Rui Xu ◽  
Ganesh Venayagamoorthy ◽  
Donald C. Wunsch
Keyword(s):  
Particle Swarm Optimization ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Gene Regulatory

Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization

2007 ◽  
Vol 20 (8) ◽  
pp. 917-927 ◽  
Author(s):  
Rui Xu ◽  
Ganesh K. Venayagamoorthy ◽  
Donald C. Wunsch
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Gene Regulatory

A sparse and decomposed particle swarm optimization for inferring gene regulatory networks based on fuzzy cognitive maps

2019 ◽  
Vol 17 (04) ◽  
pp. 1950023 ◽  
Author(s):  
Luowen Liu ◽  
Jing Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Synthetic Data ◽  
Particle Swarm ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Swarm Optimization ◽  
Gene Regulatory ◽  
Selection Operator

Inferring gene regulatory networks (GRNs) is vital to understand the complex cellular processes and reveal the regulatory mechanisms among genes. Although various methods have been developed, more accurate algorithms which can control the sparseness of GRNs still need to be developed. In this work, we model GRNs by fuzzy cognitive maps (FCMs), and a node in an FCM means a gene. Then, a new sparse and decomposed particle swarm optimization, termed as SDPSOFCM-GRN, is proposed to train FCMs, which employs the least absolute shrinkage and selection operator (Lasso) to control the network sparseness with a decomposed strategy. In the experiments, the performance of SDPSOFCM-GRN is validated on synthetic data and the well-known benchmark DREAM3 and DREAM4. The results show that SDPSOFCM-GRN can well control the sparseness of GRNs, and infer directed GRNs with high accuracy and efficiency.

Reconstruction of gene regulatory networks with multi-objective particle swarm optimisers

2020 ◽  
Author(s):  
Sandro Hurtado ◽  
José García-Nieto ◽  
Ismael Navas-Delgado ◽  
Antonio J. Nebro ◽  
José F. Aldana-Montes
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Particle Swarm ◽  
Multi Objective ◽  
Gene Regulatory
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