Abstract
Protein–protein interaction (PPI) networks are dynamic in the real world. That is, at different times and under different conditions, the interaction among proteins may or may not be active. In different dataset, PPI networks might be gathered as static or dynamic networks. For the conversion of static PPI networks to time graphs, i.e., dynamic PPI networks, additional information like gene expression and gene co-expression profiles is used. One of the challenges in system biology is to determine appropriate thresholds for converting static PPI networks to dynamic PPI networks based on active proteins. In the available methods, fixed thresholds are used for all genes. However, the purpose of this study is to determine an adaptive unique threshold for each gene. In this study, the available additional information at different times and conditions and gold-standard protein complexes was employed to determine fitting thresholds. By so doing, the problem is converted into an optimization problem. Thereafter, the problem is solved using the firefly meta-heuristic optimization algorithm. One of the most remarkable aspects of this study is determining the attractiveness function in the firefly algorithm. In this study, attraction is defined as a combination of standard complexes and gene co-expressions. Then, active proteins are specified utilizing the created thresholds. The MCL, ClusterOne, MCODE and Coach algorithms are used for final evaluation. The experimental results about BioGRID dataset and CYC2008 gold-standard protein complexes indicated that the produced dynamic PPI networks by the proposed method have better results than the earlier methods.
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References
Anthony T (2006) A brief history of systems biology. Pl Cell 18:2420–2430
De Las Rivas J, Fontanillo C (2010) Protein–protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Comput Biol 6(6):e1000807
Taheri G, Habibi M, Wong L, Eslahchi C (2013) Disruption of protein complexes. J Bioinform Comput Biol 11(03):1341008
Shen X, Yi L, Jiang X, He T, Hu X, Yang J (2016) Mining temporal protein complex based on the dynamic PIN weighted with connected affinity and gene co-expression. PLoS ONE 11(4):e0153967
Zhang Y, Du N, Li K, Feng J, Jia K, Zhang A (2013) Critical protein detection in dynamic PPI networks with multi-source integrated deep belief nets. In: IEEE international conference on bioinformatics and biomedicine (BIBM), pp 29–36
Chen B, Fan W, Liu J, Wu FX (2014) Identifying protein complexes and functional modules—from static PPI networks to dynamic PPI networks. Br Bioinform 15(2):177–194
Holme P, Saramäki J (2012) Temporal networks. Phys Rep 519(3):97–125
Wang J, Peng X, Li M, Luo Y, Pan Y (2011) Active protein interaction network and its application on protein complex detection. In: IEEE international conference on bioinformatics and biomedicine (BIBM), pp 37–42
Xu C, Tao D, Xu C (2013) A survey on multi-view learning. arXiv preprint arXiv:1304.5634
Serra A, Fratello M, Greco D, Tagliaferri R (2016) Data integration in genomics and systems biology. In: IEEE congress on evolutionary computation (CEC), pp 1272–1279
Xu C, Tao D, Li Y, Xu C (2013) Large-margin multi-view Gaussian process for image classification. In ACM proceedings of the fifth international conference on internet multimedia computing and service, pp 7–12
Wang J, Peng X, Li M, Luo Y, Pan Y (2011) Active protein interaction network and its application on protein complex detection. In: IEEE international conference on bioinformatics and biomedicine (BIBM), pp 37–42
Tang X, Wang J, Liu B, Li M, Chen G, Pan Y (2011) A comparison of the functional modules identified from time course and static PPI network data. BMC Bioinform 12(1):339
Shen X, Yi L, Jiang X, Zhao Y, Hu X, He T, Yang J (2016) Neighbor affinity based algorithm for discovering temporal protein complex from dynamic PPI network. Methods 110:90–96
Hanna EM, Zaki N, Amin A (2015) Detecting protein complexes in protein interaction networks modeled as gene expression biclusters. PLoS ONE 10(12):e0144163
Shen X, LiY, Jiang X, Zhao Y, He T, Yang J (2015) Detecting temporal protein complexes based on neighbor closeness and time course protein interaction networks. In: IEEE international conference on bioinformatics and biomedicine (BIBM), pp 109–112
Lei X, Wang F, Wu FX, Zhang A, Pedrycz W (2016) Protein complex identification through Markov clustering with firefly algorithm on dynamic protein–protein interaction networks. Inf Sci 329:303–316
Kakade SM, Foster DP (2007) Multi-view regression via canonical correlation analysis. In: International conference on computational learning theory. Springer, Berlin, pp 82–96
Akaho S (2006) A kernel method for canonical correlation analysis. arXiv preprint arXiv:cs/0609071
Xu C, Tao D, Xu C (2015) Multi-view learning with incomplete views. IEEE Trans Image Process 24(12):5812–5825
Parvin H, Alizadeh H, Fathy M, Minaei-Bidgoli B (2008) Improved face detection using spatial histogram features. In: International Conference on Image Processing, Computer Vision, & Pattern Recognition, IPCV 2008, July 14–17, 2008, Las Vegas Nevada, USA, pp 381–386. ISBN 1-60132-078-7IPCV
Parvin H, Minaei-Bidgoli B (2015) A clustering ensemble framework based on selection of fuzzy weighted clusters in a locally adaptive clustering algorithm. Pattern Anal Appl 18(1):87–112
Lee CP, Lin WS (2016) Using the two-population genetic algorithm with distance-based k-nearest neighbour voting classifier for high-dimensional data. Int J Data Min Bioinform 14(4):315–331
Zhu M, Liu S, Jiang J (2016) A hybrid method for learning multi-dimensional Bayesian network classifiers based on an optimization model. Appl Intell 44(1):123–148
Parvin H, Mohammadi M, Rezaei Z (2012) Face identification based on Gabor-wavelet features. Int J Digit Content Technol Appl 6(1):247–255
Khan MA, Shahzad W, Baig AR (2016) Protein classification via an ant-inspired association rules-based classifier. Int J Bio-Inspired Comput 8(1):51–65
Parvin H, Minaei-Bidgoli B, Alinejad-Rokny H (2013) A new imbalanced learning and dictions tree method for breast cancer diagnosis. J Bionanosci 7(6):673–678
Sun J, Garibaldi JM, Hodgman C (2012) Parameter estimation using metaheuristics in systems biology: a comprehensive review. IEEE/ACM Trans Comput Biol Bioinform (TCBB) 9(1):185–202
Adewumi AO, Arasomwan MA (2016) On the performance of particle swarm optimisation with (out) some control parameters for global optimisation. Int J Bio-Inspired Comput 8(1):14–32
Wang H, Wang W, Sun H, Rahnamayan S (2016) Firefly algorithm with random attraction. Int J Bio-Inspired Comput 8(1):33–41
Castelli M, Vanneschi L, Popovič A (2016) Parameter evaluation of geometric semantic genetic programming in pharmacokinetics. Int J Bio-Inspired Comput 8(1):42–50
Rao BS, Vaisakh K (2016) Multi-objective adaptive clonal selection algorithm for solving optimal power flow problem with load uncertainty. Int J Bio-Inspired Comput 8(2):67–83
Cai Q, Ma L, Gong M, Tian D (2016) A survey on network community detection based on evolutionary computation. Int J Bio-Inspired Comput 8(2):84–98
Junior LDRDSES, Nedjah N (2016) Distributed strategy for robots recruitment in swarm-based systems. Int J Bio-Inspired Comput 8(2):99–108
Jia Z, Duan H, Shi Y (2016) Hybrid brain storm optimisation and simulated annealing algorithm for continuous optimisation problems. Int J Bio-Inspired Comput 8(2):109–121
Srivastava PR (2016) Test case optimisation a nature inspired approach using bacteriologic algorithm. Int J Bio-Inspired Comput 8(2):122–131
Xu Z, Unveren A, Acan A (2016) Probability collectives hybridised with differential evolution for global optimisation. Int J Bio-Inspired Comput 8(3):133–153
Osuna Enciso V, Cuevas E, Oliva D, Sossa H, Pérez Cisneros M (2016) A bio-inspired evolutionary algorithm: allostatic optimization. Int J Bio-Inspired Comput 8(3):154–169
Ahirwal MK, Kumar A, Singh GK (2016) Study of ABC and PSO algorithms as optimised adaptive noise canceller for EEG/ERP. Int J Bio-Inspired Comput 8(3):170–183
Niknam T, Kavousi Fard A (2016) Optimal energy management of smart renewable micro-grids in the reconfigurable systems using adaptive harmony search algorithm. Int J Bio-Inspired Comput 8(3):184–194
Gu X (2010) Systems biology approaches to the computational modelling of trypanothione metabolism in Trypanosoma brucei. Doctoral dissertation, University of Glasgow
Fonseca R, Paluszewski M, Winter P (2010) Protein structure prediction using bee colony optimization metaheuristic. J Math Model Algorithms 9(2):181–194
Rodriguez-Fernandez M, Egea JA, Banga JR (2006) Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems. BMC Bioinform 7(1):483
Abdullah A, Deris S, Anwar S, Arjunan SN (2013) An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters. PLoS ONE 8(3):e56310
Maher B, Albrecht AA, Loomes M, Yang XS, Steinhöfel K (2014) A firefly-inspired method for protein structure prediction in lattice models. Biomolecules 4(1):56–75
Fister I, Yang XS, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46
Song L, Langfelder P, Horvath S (2012) Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinform 13(1):328
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A (2013) NCBI GEO: archive for functional genomics data sets. Nucl Acids Res 41:D991–D995
http:// www.thebiogrid.org, Accessed 29 Oct 2014
OuYang L, Dai DQ, Li XL, Wu M, Zhang XF, Yang P (2014) Detecting temporal protein complexes from dynamic protein–protein interaction networks. BMC Bioinform 15(1):335
Pu S, Wong J, Turner B, Cho E, Wodak SJ (2009) Up-to-date catalogues of yeast protein complexes. Nucl Acids Res 37(3):825–831
Van Dongen S M (2001) Graph clustering by flow simulation. Doctoral dissertation
Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform 4(1):2
Nepusz T, Yu H, Paccanaro A (2012) Detecting overlapping protein complexes in protein–protein interaction networks. Nat Methods 9(5):471–472
Wu M, Li X, Ng SK (2009) A core-attachment based method to detect protein complexes in PPI networks. BMC Bioinform 10:169
Wang J, Peng X, Li M, Pan Y (2013) Construction and application of dynamic protein interaction network based on time course gene expression data. Proteomics 13(2):301–312
Byrum S, Smart SK, Larson S, Tackett AJ (2012) Analysis of stable and transient protein–protein interactions. Methods Mol Biol 833:143–152. doi:10.1007/978-1-61779-477-3_10
http://www.ncbi.nlm.nih.gov/geo/, Accessed 29 Oct 2014
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Mohammadi Jenghara, M., Ebrahimpour-Komleh, H. & Parvin, H. Dynamic protein–protein interaction networks construction using firefly algorithm. Pattern Anal Applic 21, 1067–1081 (2018). https://doi.org/10.1007/s10044-017-0626-7
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DOI: https://doi.org/10.1007/s10044-017-0626-7