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Pattern Analysis and Applications
Published in: Pattern Analysis and Applications 4/2018

16-06-2017 | Theoretical Advances

Dynamic protein–protein interaction networks construction using firefly algorithm

Authors: Moslem Mohammadi Jenghara, Hossein Ebrahimpour-Komleh, Hamid Parvin

Published in: Pattern Analysis and Applications | Issue 4/2018

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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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Literature
1.
Anthony T (2006) A brief history of systems biology. Pl Cell 18:2420–2430CrossRef
2.
De Las Rivas J, Fontanillo C (2010) Protein–protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Comput Biol 6(6):e1000807CrossRef
3.
Taheri G, Habibi M, Wong L, Eslahchi C (2013) Disruption of protein complexes. J Bioinform Comput Biol 11(03):1341008CrossRef
4.
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):e0153967CrossRef
5.
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
6.
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):177–194CrossRef
7.
8.
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
9.
10.
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
11.
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
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
13.
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):339CrossRef
14.
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 110:90–96CrossRef
15.
Hanna EM, Zaki N, Amin A (2015) Detecting protein complexes in protein interaction networks modeled as gene expression biclusters. PLoS ONE 10(12):e0144163CrossRef
16.
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
17.
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 329:303–316CrossRef
18.
Kakade SM, Foster DP (2007) Multi-view regression via canonical correlation analysis. In: International conference on computational learning theory. Springer, Berlin, pp 82–96
19.
20.
21.
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
22.
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):87–112MathSciNetCrossRef
23.
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):315–331MathSciNetCrossRef
24.
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):123–148CrossRef
25.
Parvin H, Mohammadi M, Rezaei Z (2012) Face identification based on Gabor-wavelet features. Int J Digit Content Technol Appl 6(1):247–255CrossRef
26.
Khan MA, Shahzad W, Baig AR (2016) Protein classification via an ant-inspired association rules-based classifier. Int J Bio-Inspired Comput 8(1):51–65CrossRef
27.
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):673–678CrossRef
28.
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):185–202CrossRef
29.
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):14–32CrossRef
30.
Wang H, Wang W, Sun H, Rahnamayan S (2016) Firefly algorithm with random attraction. Int J Bio-Inspired Comput 8(1):33–41CrossRef
31.
Castelli M, Vanneschi L, Popovič A (2016) Parameter evaluation of geometric semantic genetic programming in pharmacokinetics. Int J Bio-Inspired Comput 8(1):42–50CrossRef
32.
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):67–83CrossRef
33.
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):84–98CrossRef
34.
Junior LDRDSES, Nedjah N (2016) Distributed strategy for robots recruitment in swarm-based systems. Int J Bio-Inspired Comput 8(2):99–108CrossRef
35.
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):109–121CrossRef
36.
Srivastava PR (2016) Test case optimisation a nature inspired approach using bacteriologic algorithm. Int J Bio-Inspired Comput 8(2):122–131CrossRef
37.
Xu Z, Unveren A, Acan A (2016) Probability collectives hybridised with differential evolution for global optimisation. Int J Bio-Inspired Comput 8(3):133–153CrossRef
38.
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):154–169CrossRef
39.
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):170–183CrossRef
40.
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):184–194CrossRef
41.
Gu X (2010) Systems biology approaches to the computational modelling of trypanothione metabolism in Trypanosoma brucei. Doctoral dissertation, University of Glasgow
42.
Fonseca R, Paluszewski M, Winter P (2010) Protein structure prediction using bee colony optimization metaheuristic. J Math Model Algorithms 9(2):181–194MathSciNetCrossRef
43.
Rodriguez-Fernandez M, Egea JA, Banga JR (2006) Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems. BMC Bioinform 7(1):483CrossRef
44.
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):e56310CrossRef
45.
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):56–75CrossRef
46.
Fister I, Yang XS, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46CrossRef
47.
Song L, Langfelder P, Horvath S (2012) Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinform 13(1):328CrossRef
48.
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–D995CrossRef
49.
50.
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):335CrossRef
51.
Pu S, Wong J, Turner B, Cho E, Wodak SJ (2009) Up-to-date catalogues of yeast protein complexes. Nucl Acids Res 37(3):825–831CrossRef
52.
Van Dongen S M (2001) Graph clustering by flow simulation. Doctoral dissertation
53.
Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform 4(1):2CrossRef
54.
Nepusz T, Yu H, Paccanaro A (2012) Detecting overlapping protein complexes in protein–protein interaction networks. Nat Methods 9(5):471–472CrossRef
55.
Wu M, Li X, Ng SK (2009) A core-attachment based method to detect protein complexes in PPI networks. BMC Bioinform 10:169CrossRef
56.
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):301–312CrossRef
57.
58.
Metadata
Title
Dynamic protein–protein interaction networks construction using firefly algorithm
Authors
Moslem Mohammadi Jenghara
Hossein Ebrahimpour-Komleh
Hamid Parvin
Publication date
16-06-2017
Publisher
Springer London
Published in
Pattern Analysis and Applications / Issue 4/2018
Print ISSN: 1433-7541
Electronic ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-017-0626-7

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