Abstract
Background: Time series expression data of genes contain relations among different genes, which are difficult to model precisely. Slime-forming bacteria is one of the three major harmful bacteria types in industrial circulating cooling water systems.
Objective: This study aimed at constructing gene regulation network(GRN) for slime-forming bacteria to understand the microbial fouling mechanism.
Methods: For this purpose, an Adaptive Elman Neural Network (AENN) to reveal the relationships among genes using gene expression time series is proposed. The parameters of Elman neural network were optimized adaptively by a Genetic Algorithm (GA). And a Pearson correlation analysis is applied to discover the relationships among genes. In addition, the gene expression data of slime-forming bacteria by transcriptome gene sequencing was presented.
Results: To evaluate our proposed method, we compared several alternative data-driven approaches, including a Neural Fuzzy Recurrent Network (NFRN), a basic Elman Neural Network (ENN), and an ensemble network. The experimental results of simulated and real datasets demonstrate that the proposed approach has a promising performance for modeling Gene Regulation Networks (GRNs). We also applied the proposed method for the GRN construction of slime-forming bacteria and at last a GRN for 6 genes was constructed.
Conclusion: The proposed GRN construction method can effectively extract the regulations among genes. This is also the first report to construct the GRN for slime-forming bacteria.
Keywords: Gene regulation network, elman neural network, genetic algorithm, pearson correlation analysis, yeast gene data, machine learning, slime-forming bacteria.
Graphical Abstract
[1]
Raza K. Reconstruction, Topological and gene ontology enrichment analysis of cancerous gene regulatory network modules. Curr Bioinform 2016; 11: 243-58.
[2]
Ahmad FK, Deris S, Othman NH. The inference of breast cancer metastasis through gene regulatory networks. J Biomed Inform 2012; 45(2): 350-62.
[3]
Madhamshettiwar PB, Maetschke SR, Davis MJ, Reverter A, Ragan MA. Gene regulatory network inference: Evaluation and application to ovarian cancer allows the prioritization of drug targets. Genome Med 2012; 4(5): 41-55.
[4]
Raza K, Jaiswal R. Reconstruction and analysis of cancer-specific gene regulatory networks from gene expression profiles. Int J Biosci Biochem Bioinform 2013; 3: 25-34.
[5]
Kesherwani M. N H V K, Velmurugan D. Conformational dynamics of thiM riboswitch to understand gene regulation mechanism using markov state modeling and residual fluctuation network approach. J Chem Inf Model 2018; 58(8): 1638-51.
[6]
Huang H, Liu CC, Zhou XJ. Bayesian approach to transforming public gene expression repositories into disease diagnosis databases. Proc Natl Acad Sci USA 2010; 107(15): 6823-8.
[7]
Sun D, Hurley LH. The importance of negative superhelicity in inducing the formation of G-quadruplex and i-motif structures in the c-Myc promoter: Implications for drug targeting and control of gene expression. J Med Chem 2009; 52(9): 2863-74.
[8]
Lin Q, Hou S, Guan F, Lin C. HORMAD2 methylation-mediated epigenetic regulation of gene expression in thyroid cancer. J Cell Mol Med 2018; 22(10): 4640-52.
[9]
Ivanova K, Eiermann P, Tsiockas W. Differential regulation of cGMP signaling in human melanoma cells at altered gravity: Simulated microgravity down-regulates cancer-related gene expression and motility. Microgravity Sci Technol 2018; 30: 457-67.
[10]
Wei Y, Zhou F, Zhou H, Huang J, Yu D, Wu G. Endothelial progenitor cells contribute to neovascularization of non-small cell lung cancer via histone deacetylase 7-mediated cytoskeleton regulation and angiogenic genes transcription. Int J Cancer 2018; 143(3): 657-67.
[11]
Cho CJ, Jung J, Jiang L, et al. Combinatory RNA-sequencing analyses reveal a dual mode of gene regulation by ADAR1 in gastric cancer. Dig Dis Sci 2018; 63(7): 1835-50.
[12]
Kauffman S. Homeostasis and differentiation in random genetic control networks. Nature 1969; 224(5215): 177-8.
[13]
Liu Z, He Q. A novel Boolean network for analyzing the p53 gene regulatory network. Curr Bioinform 2016; 11: 13-21.
[14]
Politano G, Savino A, Benso A, Carlo SD, Rehman HU, Vasciaveo A. Using Boolean networks to model post-transcriptional regulation in gene regulatory networks. J Comput Sci 2014; 5: 332-44.
[15]
Watanabe Y, Seno S, Takenaka Y, Matsuda H. An estimation method for inference of gene regulatory network using Bayesian network with uniting of partial problems. The Tenth Asia Pacific Bioinformatics Conference (APBC 2012). 2012 Jan 17-19; Melbourne, Australia.
[16]
Peña JM, Björkegren J, Tegnér J. Growing Bayesian network models of gene networks from seed genes. Bioinformatics 2005; 21(Suppl. 2): ii224-9.
[17]
Wang Y, Chen X, Liu ZP, et al. De novo prediction of RNA-protein interactions from sequence information. Mol Biosyst 2013; 9(1): 133-42.
[18]
Wu S, Liu ZP, Qiu X, Wu H. Modeling genome-wide dynamic regulatory network in mouse lungs with influenza infection using high-dimensional ordinary differential equations. PLoS One 2014; 9(5)e95276
[19]
Rubiolo M, Milone DH, Stegmayer G. Mining gene regulatory networks by neural modeling of expression time-series. IEEE/ACM Trans Comput Biol Bioinformatics 2015; 12(6): 1365-73.
[20]
Maraziotis IA, Dragomir A, Bezerianos A. Gene networks reconstruction and time-series prediction from microarray data using recurrent neural fuzzy networks. IET Syst Biol 2007; 1(1): 41-50.
[21]
Bu H, Gan Y, Wang Y, Zhou S, Guan J. A new method for enhancer prediction based on deep belief network. BMC Bioinformatics 2017; 18(Suppl. 12): 418.
[22]
Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol 2015; 33(8): 831-8.
[23]
Chen X, Yan CC, Zhang X, You ZH. Long non-coding RNAs and complex diseases: From experimental results to computational models. Brief Bioinform 2017; 18(4): 558-76.
[24]
Huang YA, Chan KCC, You ZH. Constructing prediction models from expression profiles for large scale lncRNA-miRNA interaction profiling. Bioinformatics 2018; 34(5): 812-9.
[25]
Li JQ, You ZH, Li X, Ming Z, Chen X. PSPEL: In silico prediction of self-interacting proteins from amino acids sequences using ensemble learning. IEEE/ACM Trans Comput Biol Bioinformatics 2017; 14(5): 1165-72.
[26]
Li JH, Liu S, Zhou H, Qu LH, Yang JH. starBase v2.0: Decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 2014; 42(Database issue): D92-7.
[27]
Yamanishi Y, Kotera M, Kanehisa M, Goto S. Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 2010; 26(12): i246-54.
[28]
Zou Q, Wan S, Zeng X, Ma ZS. Reconstructing evolutionary trees in parallel for massive sequences. BMC Syst Biol 2017; 11(Suppl. 6): 100.
[29]
Liu B, Fang L, Long R, Lan X, Chou KC. iEnhancer-2L: A two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Bioinformatics 2016; 32(3): 362-9.
[30]
Haberman Y, BenShoshan M, Di Segni A, et al. Long ncRNA Landscape in the Ileum of Treatment-Naive Early-Onset Crohn Disease. Inflamm Bowel Dis 2018; 24(2): 346-60.
[31]
Suresh V, Liu L, Adjeroh D, Zhou X. RPI-Pred: Predicting ncRNA-protein interaction using sequence and structural information. Nucleic Acids Res 2015; 43(3): 1370-9.
[32]
Li Y, Chen J, Zhang J, et al. Construction and analysis of lncRNA-lncRNA synergistic networks to reveal clinically relevant lncRNAs in cancer. Oncotarget 2015; 6(28): 25003-16.
[33]
Yang S, Ning Q, Zhang G, Sun H, Wang Z, Li Y. Construction of differential mRNA-lncRNA crosstalk networks based on ceRNA hypothesis uncover key roles of lncRNAs implicated in esophageal squamous cell carcinoma. Oncotarget 2016; 7(52): 85728-40.
[34]
Zou Q, Qu K, Luo Y, Yin D, Ju Y, Tang H. Predicting diabetes mellitus with machine learning techniques. Front Genet 2018; 9: 515.
[35]
Liu B, Wang S, Long R, Chou KC. iRSpot-EL: Identify recombination spots with an ensemble learning approach. Bioinformatics 2017; 33(1): 35-41.
[36]
Samarasinghe S, Ling H. A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks. Biosystems 2017; 153-154: 6-25.
[37]
Luo Y. Recurrent neural networks for classifying relations in clinical notes. J Biomed Inform 2017; 72: 85-95.
[38]
Shi H, Xu M, Li R. Deep learning for household load forecasting-a novel pooling deep RNN. IEEE Trans Smart Grid 2017; 9: 5271-80.
[39]
Hu YC, Lu XB. Learning spatial-temporal features for video copy detection by the combination of CNN and RNN. J Vis Commun Image Represent 2018; 55: 21-9.
[40]
Lin Z, Huang Y, Wang J. RNN-SM: fast steganalysis of voIP streams using recurrent neural network. IEEE Trans Inf Forensics Security 2018; 13: 1854-68.
[41]
Gelly G, Gauvain JL. Optimization of RNN-based speech activity detection. IEEE/ACM Trans Audio Speech Lang Process 2018; 26: 646-56.
[42]
Wang JY, Zhang C. Software reliability prediction using a deep learning model based on the RNN encoder-decoder. Reliab Eng Syst Saf 2018; 170: 73-82.
[43]
Choi M, Tani J. Predictive coding for dynamic visual processing: development of functional hierarchy in a multiple spatio-temporal scales RNN model. Neural Comput 2018; 30(1): 237-70.
[44]
Jin L, Li S, Hu B. RNN models for dynamic matrix inversion: a control-theoretical perspective. IEEE Trans Industr Inform 2018; 14: 188-99.
[45]
Mun S, Shon S, Kim W, Han DK, Ko H. A novel discriminative feature extraction for acoustic scene classification using RNN based source separation. IEICE Trans Inf Syst 2017; 100: 3041-4.
[47]
Ciarlini P, Maniscalco U. Wavelets and Elman neural networks for monitoring environmental variables. J Comput Appl Math 2008; 221: 302-9.
[48]
Liu H, Tian HQ, Liang XF, Li YF. Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks. Appl Energy 2015; 157: 183-94.
[49]
Zhao J, Zhu X, Wang W, Liu Y. Extended Kalman filter-based Elman networks for industrial time series prediction with GPU acceleration. Neurocomputing 2013; 118: 215-24.
[50]
Ruiz LGB, Rueda R, Cuéllar MP, Pegalajar MC. Energy consumption forecasting based on Elman neural networks with evolutive optimization. Expert Syst Appl 2018; 92: 380-9.
[51]
Liang L, Wu DS, Wang ZQ, Xiong L, Wang G. Research of corporate credit scoring based on ANFIS and Elman neural networks. J Ind Eng Eng Management 2005; 19: 69-73.
[52]
Edinson P, Muthuraj L. Performance analysis of FCM based ANFIS and Elman neural network in software effort estimation. Int Arab J Inf Technol 2018; 15: 94-102.
[53]
Lin CM, Boldbaatar EA. Fault accommodation control for a biped robot using a recurrent wavelet Elman neural network. IEEE Syst J 2017; 11: 2882-93.
[54]
Li X, Zhao T, Zhang J, Chen T. Predication control for indoor temperature time-delay using Elman neural network in variable air volume system. Energy Build 2017; 154: 545-52.
[55]
Achanta S, Gangashetty SV. Deep Elman recurrent neural networks for statistical parametric speech synthesis. Speech Commun 2017; 93: 31-42.
[56]
Sitharthan R, Geethanjali M. An adaptive Elman neural network with C-PSO learning algorithm based pitch angle controller for DFIG based WECS. J Vib Control 2017; 23: 716-30.
[57]
Raghu S, Sriraam N, Kumar GP. Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier. Cogn Neurodyn 2017; 11(1): 51-66.
[58]
Ge Y, Huang Y, Hao D, Li G, Li H. An indicated torque estimation method based on the Elman neural network for a turbocharged diesel engine. Proc Inst Mech Eng, D J Automob Eng 2016; 230: 1299-313.
[59]
Shen C, Song R, Li J, Zhang X, Tang J. Temperature drift modeling of MEMS gyroscope based on genetic-Elman neural network. Mech Syst Signal Process 2016; 72: 897-905.
[60]
Zhang Z, Gong W. Short-term load forecasting model based on quantum Elman neural networks. Math Probl Eng 2016; 2016: 1-8.
[61]
Wang J, Wang J, Fang W, Niu H. Financial time series prediction using Elman recurrent random neural networks. Comput Intell Neurosci 2016.20164742515
[62]
Holland JH. In: Adaptation in natural and artificial systems. University of Michigan Press Ann Arbor 1975.
[63]
Wu P, Jiang Y, Zhu L, Li X, Tang G. Optimizing the gain of social genetic effect under the control of inbreeding using genetic algorithm. Livest Sci 2016; 190: 70-5.
[64]
Owais M. Complete hierarchical multi-objective genetic algorithm for transit network design problem. Expert Syst Appl 2018; 114: 143-54.
[65]
Li Z, Elefteriadou L, Ranka S. Signal control optimization for automated vehicles at isolated signalized intersections. Transp Res, Part C Emerg Technol 2014; 49: 1-18.
[66]
Shirali A, Kordestani JK, Meybodi MR. Self-adaptive multi-population genetic algorithms for dynamic resource allocation in shared hosting platforms. Genet Program Evolvable Mach 2018; 19: 505-34.
[67]
Erdin I, Achar R. Multipin optimization method for placement of decoupling capacitors using a genetic algorithm. IEEE Trans Electromagn Compat 2018; 60: 1662-9.
[68]
Sawyerr BA, Adewumi AO, Ali MM. Real-coded genetic algorithm with uniform random local search. Appl Math Comput 2014; 228: 589-97.
[69]
Ao SI, Palade V. Ensemble of Elman neural networks and support vector machines for reverse engineering of gene regulatory networks. Appl Soft Comput 2011; 11: 1718-26.
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