Volume 10, Issue 4 (2019)
JMBS 2019, 10(4): 545-555 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Mahdevar G. Inferring Co-Expression Networks from their Associated Attrib-utes by Neural Networks. JMBS 2019; 10 (4) :545-555
URL: http://biot.modares.ac.ir/article-22-15549-en.html
URL: http://biot.modares.ac.ir/article-22-15549-en.html
Mathematics Department, Sciences Faculty, University of Isfahan, Isfahan, Iran, Mathematics Department, Sciences Faculty, University of Isfahan, Azadi Square, Isfahan, Iran. Postal Code: 8174673441 , gh.mahdevar@sci.ui.ac.ir
Abstract: (5644 Views)
Gene expression, flow of information from DNA to proteins, is a fundamental biological process. Expression of one gene can be regulated by the product of another gene. These regulatory relationships are usually modeled as a network; genes are modeled as nodes and their relationships are shown as edges. There are many efforts for discovering how genes regulate expression of themselves. This paper presents a new method that employs expression data and ontological data to infer co-expression networks, networks made by connecting genes with similar expression patterns. In brief, the method begins by learning associations between the available ontological information and the provided co-expression data. Later, the method is able to find both known and novel co-expressed pairs of genes. Finally, the method uses a self-organizing map to adjust estimation made by the previous step and to form the GCN for the input genes. The results show that the proposed method works well on the biological data and its predictions are accurate; consequently, co-expression networks generated by the proposed method are very similar to the biological networks or those that constructed with no missing data. The method is written in C++ language and is available upon request from the corresponding author.
Article Type: Original Research |
Subject:
Bioinformatics
Received: 2018/09/10 | Accepted: 2019/07/15 | Published: 2019/12/21
Received: 2018/09/10 | Accepted: 2019/07/15 | Published: 2019/12/21
References
1. Reece JB, Urry LA, Cain ML, Jackson RB, Wasserman SA, Minorsky PV. Campbell biology. 9th Edition. San Francisco: Benjamin Cummings; 2010. [Link]
2. Lodish HF, Berk A, Kaiser CA, Krieger M, Scott MP. Molecular cell biology. 6th Edition. New York: Macmillan Higher Education; 2007. [Link]
3. De Jong H. Modeling and simulation of genetic regulatory systems: A literature review. J Comput Biol. 2002;9(1):67-103. [Link] [DOI:10.1089/10665270252833208]
4. Hecker M, Lambeck S, Toepfer S, Van Someren E, Guthke R. Gene regulatory network inference: Data intgrtion in dynamic models-a review. Biosystems. 2009;96(1):86-103. [Link] [DOI:10.1016/j.biosystems.2008.12.004]
5. Reverter A, Chan EKF. Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks. Bioinformatics. 2008;24(21):2491-7. [Link] [DOI:10.1093/bioinformatics/btn482]
6. De Smet R, Marchal K. Advantages and limitations of current network inference methods. Nat Rev Micro-biol. 2010;8(10):717-29. [Link] [DOI:10.1038/nrmicro2419]
7. Kabir M, Noman N, Iba H. Reverse engineering gene regulatory network from microarray data using linear time-variant model. BMC Bioinform. 2010;11(1):S56. [Link] [DOI:10.1186/1471-2105-11-S1-S56]
8. Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, Stolovitzky G. Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci. 2010;107(14):6286-91. [Link] [DOI:10.1073/pnas.0913357107]
9. Allen JD, Xie Y, Chen M, Girard L, Xiao G. Comparing statistical methods for constructing large scale gene networks. PLoS One. 2012;7(1):e29348. [Link] [DOI:10.1371/journal.pone.0029348]
10. Schlitt T, Brazma A. Current approaches to gene regulatory network modelling. BMC Bioinform. 2007;8(6):S9. [Link] [DOI:10.1186/1471-2105-8-S6-S9]
11. Pavesi G, Valentini G. Classification of co-expressed genes from DNA regulatory regions. Inf Fusion. 2009;10(3):233-41. [Link] [DOI:10.1016/j.inffus.2008.11.005]
12. Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P. Inferring regulatory networks from expression data using tree-based methods. PLoS One. 2010;5(9):e12776. [Link] [DOI:10.1371/journal.pone.0012776]
13. Ruan J, Dean AK, Zhang W. A general co-expression network-based approach to gene expression analy-sis: Comparison and applications. BMC Syst Biol. 2010;4:8. [Link] [DOI:10.1186/1752-0509-4-8]
14. Raman K. Construction and analysis of protein-protein interaction networks. Autom Exp. 2010;2(1):2. [Link] [DOI:10.1186/1759-4499-2-2]
15. Martin Sh, Zhang Z, Martino A, Faulon JL. Boolean dynamics of genetic regulatory networks inferred from microarray time series data. Bioinformatics. 2007;23(7):866-74. [Link] [DOI:10.1093/bioinformatics/btm021]
16. Beer MA, Tavazoie S. Predicting gene expression from sequence. Cell. 2004;117(2):185-98. [Link] [DOI:10.1016/S0092-8674(04)00304-6]
17. Mahdevar G, Nowzari-Dalini A, Sadeghi M. Inferring gene correlation networks from transcription factor binding sites. Genes Genet Syst. 2013;88(5):301-9. [Link] [DOI:10.1266/ggs.88.301]
18. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: Tool for the unifica-tion of biology. Nat Genet. 2000;25(1):25-9. [Link] [DOI:10.1038/75556]
19. Rhee SY, Wood V, Dolinski K, Draghici S. Use and misuse of the gene ontology annotations. Nat Rev Genet. 2008;9(7):509-15. [Link] [DOI:10.1038/nrg2363]
20. Allocco DJ, Kohane IS, Butte AJ. Quantifying the relationship between co-expression, co-regulation and gene function. BMC Bioinform. 2004;5(1):18. [Link] [DOI:10.1186/1471-2105-5-18]
21. Luo F, Yang Y, Zhong J, Gao H, Khan L, Thompson DK, et al. Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory. BMC Bioinform. 2007;8(1):299. [Link] [DOI:10.1186/1471-2105-8-299]
22. Roy S, Bhattacharyya DK, Kalita JK. Reconstruction of gene co-expression network from microarray data using local expression patterns. BMC Bioinform. 2014;15(Suppl 7):S10. [Link] [DOI:10.1186/1471-2105-15-S7-S10]
23. Sevilla JL, Segura V, Podhorski A, Guruceaga E, Mato JM, Martinez-Cruz LA, et al. Correlation between gene expression and GO semantic similarity. IEEE ACM Trans Comput Biol Bioinform. 2005;2(4):330-8. [Link] [DOI:10.1109/TCBB.2005.50]
24. Wang H, Azuaje F, Bodenreider O, Dopazo J. Gene expression correlation and gene ontology-based simi-larity: An assessment of quantitative relationships. Symposium on Computational Intelligence in Bioinformat-ics and Computational Biology, 7-8 October, 2004, La Jolla, California, USA. Piscataway: IEEE; 2004. pp. 25-31. [Link]
25. Resnik P. Semantic similarity in a taxonomy: An information-based measure and its application to prob-lems of ambiguity in natural language. J Artif Intell Res. 1999;11:95-130. [Link] [DOI:10.1613/jair.514]
26. Wang JZ, Du Z, Payattakool R, Yu PS, Chen CF. A new method to measure the semantic similarity of GO terms. Bioinformatics. 2007;23(10):1274-81. [Link] [DOI:10.1093/bioinformatics/btm087]
27. Stuart JM, Segal E, Koller D, Kim SK. A gene-coexpression network for global discovery of conserved ge-netic modules. Science. 2003;302(5643):249-55. [Link] [DOI:10.1126/science.1087447]
28. Kohonen T. Self-organization and associative memory. 3rd Edition. Heidelberg: Springer; 1989. [Link] [DOI:10.1007/978-3-642-88163-3]
29. Shalev-Shwartz Sh, Ben-David Sh. Understanding machine learning: From theory to algorithms. 1st Edi-tion. Cambridge: Cambridge University Press; 2014. [Link]
30. Alpaydin E. Introduction to machine learning. 2nd Edition. Cambridge: MIT Press; 2010. [Link]
31. Han J, Kamber M. Data mining: Concepts and techniques. 2nd Edition. Amsterdam: Elsevier; 2006. [Link]
32. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. 2nd Edition. New York: Springer; 2009. [Link] [DOI:10.1007/978-0-387-84858-7]
33. Werbos PJ. Beyond regression: New tools for prediction and analysis in the behavioral sciences. Cam-bridge: Harvard University; 1974. [Link]
34. Barabasi AL, Oltvai ZN. Network biology: Understanding the cell's functional organization. Nat Rev Genet. 2004;5(2):101-13. [Link] [DOI:10.1038/nrg1272]
35. Bergmann S, Ihmels J, Barkai N. Similarities and differences in genome-wide expression data of six organ-ism. PLoS Biol. 2004;2(1):e9. [Link] [DOI:10.1371/journal.pbio.0020009]
36. Ma H, Zeng AP. Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics. 2003;19(2):270-7. [Link] [DOI:10.1093/bioinformatics/19.2.270]
37. Kim H, Shin J, Kim E, Kim H, Hwang S, Shim JE, et al. YeastNet v3: A public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae. Nucleic Acids Res. 2013;42(D1):D731-6. [Link] [DOI:10.1093/nar/gkt981]
38. Martínez-Ballesteros M, Nepomuceno-Chamorro IA, Riquelme JC. Discovering gene association networks by multi-objective evolutionary quantitative association rules. J Comput Syst Sci. 2014;80(1):118-36. [Link] [DOI:10.1016/j.jcss.2013.03.010]
39. Haykin S. Neural networks: A comprehensive foundation. 2nd Edition. New Jersey: Pearson Education Canada; 1998. [Link]
40. Soranzo N, Bianconi G, Altafini C. Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: Synthetic versus real data. Bioinformatics. 2007;23(13):1640-7. [Link] [DOI:10.1093/bioinformatics/btm163]
41. Soinov LA, Krestyaninova MA, Brazma A. Towards reconstruction of gene networks from expression data by supervised learning. Genome Biol. 2003;4(1):R6. [Link] [DOI:10.1186/gb-2003-4-1-r6]
42. Nepomuceno-Chamorro IA, Aguilar-Ruiz JS, Riquelme JC. Inferring gene regression networks with model trees. BMC Bioinform. 2010;11:517. [Link] [DOI:10.1186/1471-2105-11-517]
43. Bulashevska S, Eils R. Inferring genetic regulatory logic from expression data. Bioinformatics. 2005;21(11):2706-13. [Link] [DOI:10.1093/bioinformatics/bti388]
44. Ponzoni I, Azuaje F, Augusto J, Glass D. Inferring adaptive regulation thresholds and association rules from gene expression data through combinatorial optimization learning. IEEE ACM Trans Comput Biol Bioin-form. 2007;4(4):624-34. [Link] [DOI:10.1109/tcbb.2007.1049]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |