Abstract
Capturing the rules that govern a particular system can be useful in any field where the causes of its effects are unknown. Indeed, discovering the causes that produced a particular effect is extremely useful in fields such as biology. In this paper, a reverse engineering method based on machine learning is proposed. This method was used to replicate real world behaviour and use this knowledge to generate the relative Gene Regulatory Network. The datasets from the DREAM4 Challenge were used to validate this method.
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References
Alvarez, J.M., Brooks, M.D., Swift, J., Coruzzi, G.M.: Time-based systems biology approaches to capture and model dynamic gene regulatory networks. Ann. Rev. Plant Biol. 72(1) (2021). https://par.nsf.gov/biblio/10231631
Aster, R.C., Borchers, B., Thurber, C.H.: Parameter Estimation and Inverse Problems. Elsevier (2018)
Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M., Inman, D.J.: A review of vibration-based damage detection in civil structures: from traditional methods to machine learning and deep learning applications. Mech. Syst. Sign. Process. 147, 107077 (2021). https://www.sciencedirect.com/science/article/pii/S0888327020304635
Bai, Y., Chen, W., Chen, J., Guo, W.: Deep learning methods for solving linear inverse problems: Research directions and paradigms. Sign. Process. 177, 107729 (2020). https://www.sciencedirect.com/science/article/pii/S0165168420302723
Chikofsky, E., Cross, J.: Reverse engineering and design recovery: a taxonomy. IEEE Softw. 7(1), 13–17 (1990)
Cutello, V., Krasnogor, N., Nicosia, G., Pavone, M.: Immune algorithm versus differential evolution: A comparative case study using high dimensional function optimization. In: 8th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA), Vol. LNCS 4431, pp. 93–101 (2007)
Cutello, V., Morelli, G., Nicosia, G., Pavone, M., Scollo, G.: On discrete models and immunological algorithms for protein structure prediction. Nat. Comput. 10(1), 91–102 (2011). https://doi.org/10.1007/s11047-010-9196-y
Flamary, R.: Astronomical image reconstruction with convolutional neural networks. In: 2017 25th European Signal Processing Conference (EUSIPCO), pp. 2468–2472 (2017)
Groetsch, C.W., Groetsch, C.: Inverse Problems in the Mathematical Sciences, Vol. 52. Springer (1993)
Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., Guthke, R.: Gene regulatory network inference: Data integration in dynamic models-a review. Biosystems 96(1), 86–103 (2009). https://www.sciencedirect.com/science/article/pii/S0303264708002608
Huynh-Thu, V.A., Sanguinetti, G.: Gene regulatory network inference: an introductory survey. In: Gene Regulatory Networks, pp. 1–23. Springer (2019)
Kantarci, B., Labatut, V.: Classification of complex networks based on topological properties. In: 2013 International Conference on Cloud and Green Computing, pp. 297–304 (Sep 2013)
Karlebach, G., Shamir, R.: Modelling and analysis of gene regulatory networks. Nature Rev. Mol. Cell Biol. 9(10), 770–780 (2008). https://doi.org/10.1038/nrm2503
Lucas, A., Iliadis, M., Molina, R., Katsaggelos, A.K.: Using deep neural networks for inverse problems in imaging: Beyond analytical methods. IEEE Sign. Process. Magaz. 35(1), 20–36 (2018)
Mata, A.S.d.: Complex networks: a mini-review. Brazilian J. Phys. 50(5), 658–672 (2020). https://doi.org/10.1007/s13538-020-00772-9
Pilozzi, L., Farrelly, F.A., Marcucci, G., Conti, C.: Machine learning inverse problem for topological photonics. Commun. Phys. 1(1), 57 (2018). https://doi.org/10.1038/s42005-018-0058-8
Rubiolo, M., Milone, D.H., Stegmayer, G.: Extreme learning machines for reverse engineering of gene regulatory networks from expression time series. Bioinformatics 34(7), 1253–1260 (2017). https://doi.org/10.1093/bioinformatics/btx730
Shmulevich, I., Dougherty, E.R., Zhang, W.: Control of stationary behavior in probabilistic Boolean networks by means of structural intervention. J. Biol. Syst. 10(04), 431–445 (2002). https://doi.org/10.1142/S0218339002000706
Talbi, E.G.: Metaheuristics: From Design to Implementation. Wiley Publishing (2009)
Vauhkonen, M., Tarvainen, T., Lähivaara, T.: Inverse Problems, pp. 207–227. Springer International Publishing, Cham (2016), https://doi.org/10.1007/978-3-319-27836-0_12
Yaman, F., Yakhno, V.G., Potthast, R.: A survey on inverse problems for applied sciences. Math. Prob. Eng., 976837 (2013). https://doi.org/10.1155/2013/976837
Yang, Y., Yang, H.: Complex network-based time series analysis. Phys. A Stat. Mech. Appl. 387(5), 1381–1386 (2008). https://www.sciencedirect.com/science/article/pii/S0378437107011235
Zhao, M., He, W., Tang, J., Zou, Q., Guo, F.: A comprehensive overview and critical evaluation of gene regulatory network inference technologies. Brief. Bioinform. 22(5) (2021). https://doi.org/10.1093/bib/bbab009
Zito, F., Cutello, V., Pavone, M.: Optimizing multi-variable time series forecasting using metaheuristics. In: 2022, 14th Metaheuristics International Conference (MIC), vol. LNCS (to appear), pp. 1–15 (2022)
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Zito, F., Cutello, V., Pavone, M. (2023). A Novel Reverse Engineering Approach for Gene Regulatory Networks. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Miccichè, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-031-21127-0_26
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