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Reconstructing gene regulatory networks with a memetic-neural hybrid based on fuzzy cognitive maps

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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.

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References

  • Ando S, Iba H (2003) Estimation of gene regulatory network by genetic algorithm and pairwise correlation analysis. In: CEC’03, pp 207–214

  • Bakken BT, Gilljam M (2002) Training to improve decision-making-system dynamics applied to higher level military operations. In: 20th international system dynamics conference, Italy

  • Bansal M, Belcastro V, Ambesi-Impiombato A, di Bernardo D (2007) How to infer gene networks from expression profiles. Mol Syst Biol 3(1):78

    Google Scholar 

  • Chen Y, Mazlack LJ, Lu LJ (2012) Learning fuzzy cognitive maps from data by ant colony optimization. In: Proceedings of genetic and evolutionary computation conference, pp 9–16

  • Chen Y, Mazlack LJ, Lu LJ (2012) Inferring fuzzy cognitive map models for gene regulatory networks from gene expression data. In: IEEE international conference on bioinformatics and biomedicine, pp 589–601, USA

  • D’Haeseleer P, Liang S, Somogyi R (2000) Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16(8):707–726

    Google Scholar 

  • De Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9:67–103

    MathSciNet  Google Scholar 

  • De La Fuente A, Brazhnik P, Mendes P (2002) Linking the genes: inferring quantitative gene networks from microarray data. Trends Genet 18:395–398

    Google Scholar 

  • Dickerson JA, Cox Z, Wurtele ES, Fulmer AW (2001) Creating metabolic and regulatory network models using fuzzy cognitive maps. In: North American fuzzy information processing conference (NAFIPS), vol 4, pp 2171–2176

  • Eriksson R, Olsson B (2004) Adapting genetic regulatory models by genetic programming. Biosystems 76(1–3):217–227

    Google Scholar 

  • Esmaeili A, Jacob C (2009) A multi-objective differential evolutionary approach toward more stable gene regulatory networks. Biosystems 98(3):127–136

    Google Scholar 

  • Fomekong-Nanfack Y, Kaandorp JA, Blom J (2007) Efficient parameter estimation for spatio-temporal models of pattern formation: case study of Drosophila melanogaster. Bioinformatics 23(24):3356–3363

    Google Scholar 

  • Friedman N, Linial M, Nachman I, Pe’er D (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7:601–620

    Google Scholar 

  • Froelich W, Wakulicz-Deja A (2009) Mining temporal medical data using adaptive fuzzy cognitive maps. In: Proceedings of conference on human systems and interactions, Catania, Italy, pp 13–20

  • Georgopoulos VC, Malandraki GA, Stylios CD (2003) A fuzzy cognitive map approach to differential diagnosis of specific language impairment. J Artif Intell Med 29(3):261–278

    Google Scholar 

  • Greenfield A, Madar A, Ostrer H, Bonneau R (2010) DREAM4: combining genetic and dynamic information to identify biological networks and dynamical models. PLoS ONE 5:e13397

    Google Scholar 

  • Hovatta I, Kimppa K, Lehmussola A et al (2005) DNA microarray data analysis, 2nd edn. CSC, Scientific Computing Ltd, Finland

    Google Scholar 

  • Iba H, Mimura A (2002) Inference of a gene regulatory network by means of interactive evolutionary computing. Inf Sci 145(3–4):225–236

    Google Scholar 

  • Karlebach G, Shamir R (2008) Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 9:770–780

    Google Scholar 

  • Kauffman S, Peterson C, Samuelsson B, Troein C (2003) Random Boolean network models and the yeast transcriptional network. PNAS 100:14796–14799

    Google Scholar 

  • Keedwell E, Narayanan A (2005) Discovering gene networks with a neural-genetic hybrid. IEEE/ACM Trans Comput Biol Bioinf 2(3):231–242

    Google Scholar 

  • Kim SY, Imoto S, Miyano S (2003) Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief Bioinform 4:228–235

    Google Scholar 

  • Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24:65–75

    MATH  Google Scholar 

  • Koulouriotis DE, Diakoulakis IE, Emiris DM (2001) Learning fuzzy cognitive maps using evolution strategies: a novel schema for modeling and simulating high-level behavior. In: IEEE congress on evolutionary computation, Korea, pp 364–371

  • Krasnogor N (1999) Coevolution of genes and memes in memetic algorithms. Graduate student workshop, vol 371

  • Lee WP, Hsiao YT (2012) Inferring gene regulatory networks using a hybrid GA-PSO approach with numerical constraints and network decomposition. Inf Sci 188:80–99

    Google Scholar 

  • Linden R, Bhaya A (2007) Evolving fuzzy rules to model gene expression. Biosystems 88(1–2):76–91

    Google Scholar 

  • Liu X, Fu J, Gu D et al (2008) Genome-wide analysis of gene expression profiles during the kernel development of maize (Zea mays L.). Genomics 91(4):378–387

    Google Scholar 

  • Marbach D, Schaffter T, Mattiussi C, Floreano D (2009) Generating realistic in silico gene networks for performance assessment of reverse engineering methods. J Comput Biol 16(2):229–239

    Google Scholar 

  • Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program (report 826)

  • Noman N, Iba H (2007) Inferring gene regulatory networks using differential evolution with local search heuristics. IEEE/ACM Trans Comput Biol Bioinform 4(4):634–647

    Google Scholar 

  • Noman N, Iba H (2008) Inference of genetic networks using S-system: information criteria for model selection. In: 8th annual conference on genetic and evolutionary computation, USA, pp 263–270

  • Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern B 36(1):141

    Google Scholar 

  • Papageorgiou EI (2011) A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Appl Soft Comput 11:500–513

    Google Scholar 

  • Papageorgiou EI (2012) “Learning algorithms for fuzzy cognitive maps - a review study”, IEEE Trans. Syst Man Cybern C 42(2):150–163

    Google Scholar 

  • Papageorgiou E, Groumpos P (2004) A weight adaptation method for fuzzy cognitive maps to a process control problem. In: Lecture notes in computer science, Springer, Berlin, vol 3037

  • Papageorgiou EI, Stylios CD, Groumpos PP (2003) Fuzzy cognitive map learning based on nonlinear Hebbian rule. In: Proceedings of Australian conference on artificial intelligence, pp 256–268

  • Papageorgiou EI, Stylios CD, Groumpos PP (2004) Active Hebbian learning algorithm to train fuzzy cognitive maps. Int J Approx Reason 37(3):219–249

    MathSciNet  MATH  Google Scholar 

  • Papakostas G, Boutalis Y, Koulouriotis D, Mertzios B (2008) Fuzzy cognitive maps for pattern recognition applications. Int J Pattern Recognit Artif Intell 22(8):1461–1486

    Google Scholar 

  • Parsopoulos KE, Papageorgiou EI, Groumpos PP, Vrahatis MN (2003) A first study of fuzzy cognitive maps learning using particle swarm optimization. In: Proceedings of the 2003 congress on evolutionary computation, pp 1440–1447

  • Penfold CA, Wild DL (2011) How to infer gene networks from expression profiles, revisited. Interface Focus 1:857–870

    Google Scholar 

  • Przytycka TM, Singh M, Slonim DK (2010) Toward the dynamic interactome: it’s about time. Brief Bioinform 11(1):15–29

    Google Scholar 

  • Repsilber D, Liljenstrom H, Andersson SGE (2002) Reverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypotheses. Biosystems 66(1–2):31–41

    Google Scholar 

  • Sîrbu A, Ruskin HJ, Crane M (2010) Comparison of evolutionary algorithms in gene regulatory network model inference. BMC Bioinform 11(1):1

    Google Scholar 

  • Sîrbu A, Ruskin HJ, Crane M (2011) Stages of gene regulatory network inference: the evolutionary algorithm role. In: Kita E (ed) Evolutionary algorithms. InTech, Croatia

    Google Scholar 

  • Song H, Miao C, Roel W, Shen Z, Catthoor F (2010) Implementation of fuzzy cognitive maps based on fuzzy neural network and application in prediction of time series. IEEE Trans Fuzzy Syst 18(2):233–250

    Google Scholar 

  • Stach W (2010) Learning and aggregation of fuzzy cognitive maps—an evolutionary approach. Ph.D. dissertation, University of Alberta

  • Stach W, Kurgan L (2004) Modeling software development project using fuzzy cognitive maps. In: Proceedings of 4th ASERC workshop on quantitative and soft software engineering (QSSE’04), pp 55–60

  • Stach W, Kurgan L, Pedrycz W, Reformat M (2004) Parallel fuzzy cognitive maps as a tool for modeling software development project. In: NAFIPS’04, Banff, AB, pp 28–33

  • Stach W, Kurgan L, Pedrycz W, Reformat M (2005a) Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst 153(3):371–401

    MathSciNet  MATH  Google Scholar 

  • Stach W, Kurgan L, Pedrycz W, Reformat M (2005) Evolutionary development of fuzzy cognitive maps. In: Proceedings of the IEEE international conference on fuzzy systems, USA, pp 619–624

  • Stach W, Kurgan L, Pedrycz W (2007) Parallel learning of large fuzzy cognitive maps. In: Proceedings of international joint conference on neural networks, pp 1584–1589

  • Stach W, Kurgan LA, Pedrycz W (2008a) Numerical and linguistic prediction of time series with the use of fuzzy cognitive maps. IEEE Trans Fuzzy Syst 16(1):61–72

    Google Scholar 

  • Stach W, Kurgan L, Pedrycz W (2008) Data driven nonlinear Hebbian learning method for fuzzy cognitive maps. In: Proceedings of IEEE world congress on computational intelligence, Hong Kong, pp 1975–1981

  • Stach W, Kurgan L, Pedrycz W (2010) A divide and conquer method for learning large fuzzy cognitive maps. Fuzzy Sets Syst 161:2515–2532

    MathSciNet  MATH  Google Scholar 

  • Stolovitzky G, Monroe D, Califano A (2007) Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference. In: Stolovitzky G, Califano A (eds) Ann NY Acad Sci 1115:11–22

  • Stolovitzky G, Prill RJ, Califano A (2009) Lessons from the DREAM2 challenges. Ann NY Acad Sci 1158:159–195

    Google Scholar 

  • Tominaga D, Okamoto M, Maki Y et al (2009) Nonlinear numerical optimization technique based on a genetic algorithm for inverse problems: towards stages of gene regulatory network inference: the evolutionary algorithm role. In: GCB99 German conference on bioinformatics, pp 101–111

  • Tsadiras K (2008) Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps. Inf Sci 178:3880–3894

    Google Scholar 

  • Wang H, Qian L, Dougherty E (2010) Inference of gene regulatory networks using S-system: a unified approach. IET Syst Biol 4:145–156

    Google Scholar 

  • Xu R, Hu X, Wunsch D II (2004) Inference of genetic regulatory networks from time series gene expression data. In: Proceedings of international joint conference on neural networks, vol 2, pp 1215–1220

Download references

Acknowledgments

This work is partially supported by the Outstanding Young Scholar Program of National Natural Science Foundation of China (NSFC) under Grant 61522311, the General Program of NSFC under Grant 61271301, the Overseas, Hong Kong & Macao Scholars Collaborated Research Program of NSFC under Grant 61528205, the Research Fund for the Doctoral Program of Higher Education of China under Grant 20130203110010, and the Fundamental Research Funds for the Central Universities under Grant K5051202052.

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  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, 710071, China

    Yaxiong Chi & Jing Liu

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  1. Yaxiong Chi
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Correspondence to Jing Liu.

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Chi, Y., Liu, J. Reconstructing gene regulatory networks with a memetic-neural hybrid based on fuzzy cognitive maps. Nat Comput 18, 301–312 (2019). https://doi.org/10.1007/s11047-016-9547-4

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