Improved Firefly Optimization for Pairwise Network Alignment with its Biological Significance of Predicting GO Functions and KEGG Pathways

Global pairwise biological network alignment is a pervasive technique in bioinformatics and computational biology. Even now, the computation of network alignment is a challenging effort for delivering an efficient and statistically significant results. Thus, the optimization algorithms have been used to get the precise results of protein network alignment. In this work, an Improved Firefly Optimization Algorithm method was used to align the biological protein networks in a pairwise technique which resulted in an optimal solution. By utilizing the final outcome of network alignment, the function of proteins in a network and KEGG pathways was also obtained and found that the aligned proteins have more functions that are common in nature.

[1]  S. B. Needleman,et al.  A general method applicable to the search for similarities in the amino acid sequence of two proteins. , 1970, Journal of molecular biology.

[2]  Bonnie Berger,et al.  Global alignment of multiple protein interaction networks with application to functional orthology detection , 2008, Proceedings of the National Academy of Sciences.

[3]  Matthias Grossglauser,et al.  PROPER: global protein interaction network alignment through percolation matching , 2016, BMC Bioinformatics.

[4]  Jan Martens,et al.  NABEECO: biological network alignment with bee colony optimization algorithm , 2013, GECCO.

[5]  Natasa Przulj,et al.  L-GRAAL: Lagrangian graphlet-based network aligner , 2015, Bioinform..

[6]  Christie S. Chang,et al.  The BioGRID interaction database: 2013 update , 2012, Nucleic Acids Res..

[7]  Huan Hoang Xuan,et al.  ACOGNA: An efficient method for protein-protein interaction network alignment , 2016, 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE).

[8]  Jugal K. Kalita,et al.  Global Alignment of Protein-Protein Interaction Networks: A Survey , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[9]  Damian Szklarczyk,et al.  STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets , 2018, Nucleic Acids Res..

[10]  Pietro Hiram Guzzi,et al.  Improving the Robustness of Local Network Alignment: Design and Extensive Assessmentof a Markov Clustering-Based Approach , 2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[11]  Natasa Przulj,et al.  Integrative network alignment reveals large regions of global network similarity in yeast and human , 2011, Bioinform..

[12]  Jugal K. Kalita,et al.  A multiobjective memetic algorithm for PPI network alignment , 2015, Bioinform..

[13]  Kristina Ban,et al.  Unified Alignment of Protein-Protein Interaction Networks , 2017, Scientific Reports.

[14]  Tijana Milenkovic,et al.  MAGNA: Maximizing Accuracy in Global Network Alignment , 2013, Bioinform..

[15]  Tolga Can,et al.  SUMONA: A supervised method for optimizing network alignment , 2016, Comput. Biol. Chem..

[16]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[17]  Cheng-Yu Ma,et al.  Optimizing a global alignment of protein interaction networks , 2013, Bioinform..

[18]  Rafael C. Jimenez,et al.  The IntAct molecular interaction database in 2012 , 2011, Nucleic Acids Res..

[19]  Jianzhu Ma,et al.  ModuleAlign: module-based global alignment of protein-protein interaction networks , 2016, Bioinform..

[20]  Daisuke Kihara,et al.  NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology , 2017, BMC Bioinformatics.

[21]  Vesna Memisevic,et al.  Global G RAph A Lignment of Biological Networks , 2022 .

[22]  Daisuke Kihara,et al.  IAS: Interaction Specific GO Term Associations for Predicting Protein-Protein Interaction Networks , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[23]  Maoguo Gong,et al.  A Global Network Alignment Method Using Discrete Particle Swarm Optimization , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[24]  O. Kuchaiev,et al.  Topological network alignment uncovers biological function and phylogeny , 2008, Journal of The Royal Society Interface.

[25]  Rohit Salgotra,et al.  An improved firefly algorithm for numerical optimization , 2017, 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS).

[26]  Jie Tang,et al.  Simultaneous Optimization of both Node and Edge Conservation in Network Alignment via WAVE , 2014, WABI.

[27]  Robert Patro,et al.  Global network alignment using multiscale spectral signatures , 2012, Bioinform..

[28]  Tijana Milenkovic,et al.  MAGNA++: Maximizing Accuracy in Global Network Alignment via both node and edge conservation , 2015, Bioinform..

[29]  Wayne B. Hayes,et al.  SANA: simulated annealing far outperforms many other search algorithms for biological network alignment , 2017, Bioinform..

[30]  M. Cannataro,et al.  AlignNemo: A Local Network Alignment Method to Integrate Homology and Topology , 2012, PloS one.