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Mohammadi Jenghara, Moslem, Iranpour Mobarakeh, Majid, Ebrahimpour Komleh, Hossein. (1400). Relation Extraction of Protein Complexes from Dynamic Protein-Protein Interaction Network. سامانه مدیریت نشریات علمی, 11(6), 675-684. doi: 10.31661/jbpe.v0i0.1119
Moslem Mohammadi Jenghara; Majid Iranpour Mobarakeh; Hossein Ebrahimpour Komleh. "Relation Extraction of Protein Complexes from Dynamic Protein-Protein Interaction Network". سامانه مدیریت نشریات علمی, 11, 6, 1400, 675-684. doi: 10.31661/jbpe.v0i0.1119
Mohammadi Jenghara, Moslem, Iranpour Mobarakeh, Majid, Ebrahimpour Komleh, Hossein. (1400). 'Relation Extraction of Protein Complexes from Dynamic Protein-Protein Interaction Network', سامانه مدیریت نشریات علمی, 11(6), pp. 675-684. doi: 10.31661/jbpe.v0i0.1119
Mohammadi Jenghara, Moslem, Iranpour Mobarakeh, Majid, Ebrahimpour Komleh, Hossein. Relation Extraction of Protein Complexes from Dynamic Protein-Protein Interaction Network. سامانه مدیریت نشریات علمی, 1400; 11(6): 675-684. doi: 10.31661/jbpe.v0i0.1119

Relation Extraction of Protein Complexes from Dynamic Protein-Protein Interaction Network

Journal of Biomedical Physics and Engineering
مقاله 2، دوره 11، شماره 6، بهمن و اسفند 2021، صفحه 675-684    اصل مقاله (1.34 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.1119
نویسندگان
Moslem Mohammadi Jenghara* 1؛ Majid Iranpour Mobarakeh1؛ Hossein Ebrahimpour Komleh2
1PhD, Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran
2PhD, Department of Computer and Electrical Engineering, University of Kashan, Kashan, Iran
چکیده
Background: Dynamic protein-protein interaction networks (DPPIN) can confirm the conditional and temporal features of proteins and protein complexes. In addition, the relation of protein complexes in dynamic networks can provide useful information in understanding the dynamic functionality of PPI networks.
Objective: In this paper, an algorithm is presented to discover the temporal association rule from the dynamic PPIN dataset.
Material and Methods: In this analytical study, the static protein-protein interaction network is transformed into a dynamic network using the gene expression thresholding to extract the protein complex relations. The number of presented proteins of the dynamic network is large at each time point. This number will increase for extraction of multidimensional rules at different times. By mapping the gold standard protein complexes as reference protein complexes, the number of items decreases from active proteins to protein complexes at each transaction. Extracted sub graphs as protein complexes, at each time point, are weighted according to the reference protein complexes similarity degrees. Mega-transactions and extended items are created based on occurrence bitmap matrix of the reference complexes. Rules will be extracted based on Mega-transactions of protein complexes.
Results: The proposed method has been evaluated using gold standard protein complex rules. The amount of extracted rules from Biogrid datasets and protein complexes are 281, with support 0.2.
Conclusion: The characteristic of the proposed algorithm is the simultaneous extraction of intra-transaction and inter-transaction rules. The results evaluation using EBI data shows the efficiency of the proposed algorithm.
کلیدواژه‌ها
Protein Interaction Network؛ Temporal Association Rule؛ Dynamic Network؛ Protein Complexes؛ Proteins؛ Algorithms؛ Gene Expression
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مراجع
  1. Zaman E. Disease Similarity Using Biological Module Dysregulation Profile. North Dakota State University; 2016. Available from: https://library.ndsu.edu/ir/handle/10365/25887.
  2. Taheri G, Habibi M, Wong L, Eslahchi C. Disruption of protein complexes. Journal of Bioinformatics and Computational Biology. 2013;11(3):1341008. doi: 10.1142/S0219720013410084. PubMed PMID: 23796185.
  3. Shen X, Yi L, Jiang X, He T, Hu X, Yang J. Mining temporal protein complex based on the dynamic pin weighted with connected affinity and gene co-expression. PloS One. 2016;11(4):e0153967. doi: 10.1371/journal.pone.0153967.
  4. Zhang Y, Du N, Li K, Feng J, Jia K, Zhang A. Critical protein detection in dynamic PPI networks with multi-source integrated deep belief nets. IEEE International Conference on Bioinformatics and Biomedicine; Shanghai, China: IEEE; 2013. p. 29-36. doi: 10.1109/BIBM.2013.6732606.
  5. Morra G, Genoni A, Colombo G. Protein dynamics and drug design: The role of molecular simulations. Protein-protein Complexes. 2010:340-85. doi: 10.1142/9781848163409_0013.
  6. Mallik S, Mukhopadhyay A, Maulik U. RANWAR: rank-based weighted association rule mining from gene expression and methylation data. IEEE Transactions on Nanobioscience. 2014;14(1):59-66. doi: 10.1109/TNB.2014.2359494.
  7. Wani G, Joshi M. Quantitative estimation of time interval of 3-sequences. 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO); Noida, India: IEEE; 2016. p. 441-6. doi: 10.1109/ICRITO.2016.7784996.
  8. Lu H, Han J, Feng L. Stock movement prediction and n-dimensional inter-transaction association rules. ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery; New York, USA: ACM; 1998.
  9. Nam H, Lee K, Lee D. Identification of temporal association rules from time-series microarray data set: temporal association rules. Proceedings of the 2nd international workshop on Data and text mining in bioinformatics; New York, NY, United States: Association for Computing Machinery; 2008. p. 21-8. doi: 10.1145/1458449.1458457.
  10. Zakaria W, Kotb Y, Ghaleb F. MCR-Miner: Maximal confident association rules miner algorithm for up/down-expressed genes. Applied Mathematics & Information Sciences. 2014;8(2):799-809. doi: 10.12785/AMIS/080241.
  11. Nacher JC, Schwartz JM. Modularity in protein complex and drug interactions reveals new polypharmacological properties. PloS One. 2012;7(1):e30028. doi: 10.1371/journal.pone.0030028.
  12. Ray S, Hossain A, Maulik U. Disease associated protein complex detection: a multi-objective evolutionary approach. 2016 International conference on microelectronics, computing and communications (MicroCom); Durgapur, India: IEEE; 2016. p. 1-6. doi: 10.1109/MicroCom.2016.7522420.
  13. Moarefi I. Protein Complex Production from the Drug Discovery Standpoint. Adv Exp Med Biol. 2016;896:3-13. PubMed PMID: 27165315. doi: 10.1007/978-3-319-27216-0_1.
  14. Jin H, Chen J, He H, Williams GJ, Kelman C, O’Keefe CM. Mining unexpected temporal associations: applications in detecting adverse drug reactions. IEEE Trans Inf Technol Biomed. 2008;12(4):488-500. doi: 10.1109/TITB.2007.900808.
  15. Steinway SN, Wang RS, Albert R. Discrete dynamic modeling: a network approach for systems pharmacology. Cham: Springer; 2016. p. 81-103.
  16. Besemann C, Denton A, Yekkirala A. Differential association rule mining for the study of protein-protein interaction networks. Proceedings of the 4th International Conference on Data Mining in Bioinformatics; Berlin, Heidelberg: Springer-Verlag; 2004. p. 72-80.
  17. Gallo CA, Carballido JA, Ponzoni I. Discovering time-lagged rules from microarray data using gene profile classifiers. BMC Bioinformatics. 2011;12(1):123. doi:10.1186/1471-2105-12-123.
  18. Chen SC, Tsai TH, Chung CH, Li WH. Dynamic association rules for gene expression data analysis. BMC Genomics. 2015;16(1):786. doi: 10.1186/s12864-015-1970-x.
  19. Mohammadi-Jenghara M, Ebrahimpour-Komleh H. Extraction of Co-Behaving Genes by Similarity Ensembles. Journal of Biological Systems. 2017;25(03):479-94. doi: 10.1142/S021833901750022X.
  20. Zhou A, Zhou S, Jin W, Tian Z. Generalized multidimensional association rules. Journal of Computer Science and Technology. 2000;15(4):388-92. doi: 10.1007/BF02948876.
  21. Lee AJ, Wang CS, Weng WY, Chen YA, Wu HW. An efficient algorithm for mining closed inter-transaction itemsets. Data & Knowledge Engineering. 2008;66(1):68-91. doi: 10.1016/j.datak.2008.02.001.
  22. Tung AK, Lu H, Han J, Feng L. Breaking the barrier of transactions: Mining inter-transaction association rules. Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining; New York, USA: ACM; 1999. p. 297-301. doi: 10.1145/312129.312258.
  23. Li Q, Feng L, Wong A. From intra-transaction to generalized inter-transaction: Landscaping multidimensional contexts in association rule mining. Information Sciences. 2005;172(3-4):361-95. doi: 10.1016/j.ins.2004.07.006.
  24. Baralis E, Bruno G, Ficarra E. Temporal association rules for gene regulatory networks. 4th International IEEE Conference Intelligent Systems; Varna, Bulgaria: IEEE; 2008. p. 2-12. doi: 10.1109/IS.2008.4670511.
  25. Jenghara MM, Ebrahimpour-Komleh H, Parvin H. Dynamic protein–protein interaction networks construction using firefly algorithm. Pattern Analysis and Applications. 2018;21(4):1067-81. doi: 10.1007/s10044-017-0626-7.
  26. Wang J, Peng X, Li M, Luo Y, Pan Y. Active protein interaction network and its application on protein complex detection. International Conference on Bioinformatics and Biomedicine; Atlanta, USA: IEEE; 2011. p. 37-42. doi: 10.1109/BIBM.2011.45.
  27. Agrawal R, Srikant R. Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases; San Francisco, CA, USA: Morgan Kaufmann Publishers Inc; 1994. p. 487-99.
  28. Chatr-Aryamontri A, Oughtred R, Boucher L, et al. The BioGRID interaction database: 2017 update. Nucleic Acids Res. 2017;45(D1):D369-79. doi: 10.1093/nar/gkw1102. PubMed PMID: 27980099. PubMed PMCID: PMC5210573.
  29. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research. 2002;30(1):207-10. doi: 10.1093/nar/30.1.207.
  30. Barrett T, Wilhite SE, Ledoux P, Evangelista C, et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Research. 2012;41(D1):D991-5. doi: 10.1093/nar/gks1193.
  31. Rustici G, Mata J, Kivinen K, Lió P, Penkett CJ, et al. Periodic gene expression program of the fission yeast cell cycle. Nature Genetics. 2004;36(8):809-17. doi: 10.1038/ng1377.
  32. Cook CE, Bergman MT, Cochrane G, Apweiler R, Birney E. The European Bioinformatics Institute in 2017: data coordination and integration. Nucleic Acids Research. 2018;46(D1):D21-9. doi: 10.1093/nar/gkx1154.
  33. Dolinski K, Dwight SS, Eppig JT, Harris MA, et al. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25-9. doi: 10.1038/75556. PubMed PMID: 10802651. PubMed PMCID: PMC3037419.
  34. Huntley RP, Sawford T, Mutowo-Meullenet P, Shypitsyna A, et al. The GOA database: gene ontology annotation updates for 2015. Nucleic Acids Research. 2015;43(D1):D1057-63. doi: 10.1093/nar/gku1113. PubMed PMID: 25378336. PubMed PMCID: PMC4383930.
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