Learning Large-Scale Fuzzy Cognitive Maps Based on Compressed Sensing and Application in Reconstructing Gene Regulatory Networks

2017 ◽  
Vol 25 (6) ◽  
pp. 1546-1560 ◽  
Author(s):  
Kai Wu ◽  
Jing Liu
Keyword(s):  
Compressed Sensing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Gene Regulatory

Improved Fuzzy Cognitive Maps for Gene Regulatory Networks Inference Based on Time Series Data

2021 ◽  
Author(s):  
Marzieh Emadi ◽  
Farsad Zamani Boroujeni ◽  
jamshid Pirgazi
Keyword(s):  
Time Series ◽  
Compressed Sensing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Cognitive Maps ◽  
Boolean Networks ◽  
Fuzzy Cognitive Maps ◽  
Series Data ◽  
Gene Regulatory

Abstract Recently with the advancement of high-throughput sequencing, gene regulatory network inference has turned into an interesting subject in bioinformatics and system biology. But there are many challenges in the field such as noisy data, uncertainty, time-series data with numerous gene numbers and low data, time complexity and so on. In recent years, many research works have been conducted to tackle these challenges, resulting in different methods in gene regulatory networks inference. A number of models have been used in modeling of the gene regulatory networks including Boolean networks, Bayesian networks, Markov model, relational networks, state space model, differential equations model, artificial neural networks and so on. In this paper, the fuzzy cognitive maps are used to model gene regulatory networks because of their dynamic nature and learning capabilities for handling non-linearity and inherent uncertainty. Fuzzy cognitive maps belong to the family of recurrent networks and are well-suited for gene regulatory networks. In this research study, the Kalman filtered compressed sensing is used to infer the fuzzy cognitive map for the gene regulatory networks. This approach, using the advantages of compressed sensing and Kalman filters, allows robustness to noise and learning of sparse gene regulatory networks from data with high gene number and low samples. In the proposed method, stream data and previous knowledge can be used in the inference process. Furthermore, compressed sensing finds likely edges and Kalman filter estimates their weights. The proposed approach uses a novel method to decrease the noise of data. The proposed method is compared to CSFCM, LASSOFCM, KFRegular, ABC, RCGA, ICLA, and CMI2NI. The results show that the proposed approach is superior to the other approaches in fuzzy cognitive maps learning. This behavior is related to the stability against noise and offers a proper balance between data error and network structure.

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.

scholarly journals Mapping Gene Regulatory Networks in Drosophila Eye Development by Large-Scale Transcriptome Perturbations and Motif Inference

Cell Reports ◽  
2014 ◽  
Vol 9 (6) ◽  
pp. 2290-2303 ◽  
Author(s):  
Delphine Potier ◽  
Kristofer Davie ◽  
Gert Hulselmans ◽  
Marina Naval Sanchez ◽  
Lotte Haagen ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Eye Development ◽  
Drosophila Eye ◽  
Mapping Gene ◽  
Gene Regulatory
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