Ensemble multi‐objective evolutionary algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps. Issue 1 (20th February 2019)

• Record Type: Journal Article
• Authors: Liu, Jing; Chi, Yaxiong; Liu, Zongdong; He, Shan
• Is Part Of: CAAI transactions on intelligence technology. Volume 4:Issue 1(2019)
• Page Start: 24
• Page End: 36
• Publication Date: 2019-02-20
• Subjects: genetics -- fuzzy set theory -- evolutionary computation -- biology computing -- genomics -- learning (artificial intelligence) -- optimisation; MOEA -- historical data -- target network -- distinct optimal local information -- efficient ensemble strategy -- selected networks -- final network -- synthetic FCMs -- FCM ‐GRN -- GRNs -- ensemble multiobjective evolutionary algorithm -- gene regulatory network reconstruction -- gene expression -- genomic methods -- complicated regulatory relationships -- simple but powerful tool -- called fuzzy cognitive maps -- gene regulatory networks -- automated methods -- training FCMs -- observed time sequence data -- FCM learning problem -- network structure information -- EMOEA; A0210 Algebra, set theory, and graph theory -- A0250 Probability theory, stochastic processes, and statistics -- A8715B Biomolecular structure, configuration, conformation, and active sites -- A8725F Physics of subcellular structures -- C1140Z Other topics in statistics -- C1160 Combinatorial mathematics -- C1180 Optimisation techniques -- C4130 Interpolation and function approximation (numerical analysis) -- C6130 Data handling techniques -- C6170K Knowledge engineering techniques -- C7330 Biology and medical computing -- A8780S Genomic techniques; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2018.1059
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : 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.