Paper Info
Title
Reconstructing gene regulatory networks with a memetic-neural hybrid based on fuzzy cognitive maps
Abstract
Reconstructing gene regulatory networks (GRNs) plays an important role in identifying the complicated regulatory relationships, uncovering regulatory patterns in cells, and gaining a systematic view for biological processes. In order to reconstruct large-scale GRNs accurately, in this paper, we first use fuzzy cognitive maps (FCMs), which are a kind of cognition fuzzy influence graphs based on fuzzy logic and neural networks, to model GRNs. Then, a novel hybrid method is proposed to reconstruct GRNs from time series expression profiles using memetic algorithm (MA) combined with neural network (NN), which is labeled as MANNFCM-GRN. In MANNFCM-GRN, the MA is used to determine regulatory connections in GRNs and the NN is used to determine the interaction strength of the regulatory connections. In the experiments, the performance of MANNFCM-GRN is validated on both synthetic data and the benchmark dataset DREAM3 and DREAM4. The experimental results demonstrate the efficacy of MANNFCM-GRN and show that MANNFCM-GRN can reconstruct GRNs with high accuracy without expert knowledge. The comparison with existing algorithms also shows that MANNFCM-GRN outperforms ant colony optimization, non-linear Hebbian learning, and real-coded genetic algorithms.
Year
DOI
Venue
2019
10.1007/s11047-016-9547-4
Natural Computing
Keywords
Field
DocType
Gene regulatory networks, Fuzzy cognitive maps, Memetic algorithms, Neural networks
Ant colony optimization algorithms,Memetic algorithm,Computer science,Fuzzy cognitive map,Fuzzy logic,Hebbian theory,Artificial intelligence,Artificial neural network,Gene regulatory network,Machine learning,Genetic algorithm
Journal
Volume
Issue
ISSN
18
2
1572-9796
Citations 
PageRank 
References 
3
0.38
17
Authors
2
Name
Order
Citations
PageRank
Yaxiong Chi1502.33
Jing Liu21043115.54