Ensemble multi-objective evolutionary algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps

Jing Liu*, Yaxiong Chi, Zongdong Liu, Shan He

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


Many methods aim to use data, especially data about gene expression based on high throughput genomic methods, to identify complicated regulatory relationships between genes. The authors employ a simple but powerful tool, called fuzzy cognitive maps (FCMs), to accurately reconstruct gene regulatory networks (GRNs). Many automated methods have been carried out for training FCMs from data. These methods focus on simulating the observed time sequence data, but neglect the optimisation of network structure. In fact, the FCM learning problem is multi-objective which contains network structure information, thus, the authors propose a new algorithm combining ensemble strategy and multi-objective evolutionary algorithm (MOEA), called EMOEAFCM-GRN, to reconstruct GRNs based on FCMs. In EMOEAFCM-GRN, the MOEA first learns a series of networks with different structures by analysing historical data simultaneously, which is helpful in finding the target network with distinct optimal local information. Then, the networks which receive small simulation error on the training set are selected from the Pareto front and an efficient ensemble strategy is provided to combine these selected networks to the final network. The experiments on the DREAM4 challenge and synthetic FCMs illustrate that EMOEAFCM-GRN is efficient and able to reconstruct GRNs accurately.

Original languageEnglish
Pages (from-to)24-36
Number of pages13
JournalCAAI Transactions on Intelligence Technology
Issue number1
Publication statusPublished - 1 Mar 2019

Bibliographical note

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Information Systems


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