Top

Published in:

2024 | OriginalPaper | Chapter

Exploiting Medical-Expert Knowledge Via a Novel Memetic Algorithm for the Inference of Gene Regulatory Networks

Authors : Adrián Segura-Ortiz, José García-Nieto, José F. Aldana-Montes

Published in: Computational Science – ICCS 2024

Publisher: Springer Nature Switzerland

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This study introduces an innovative memetic algorithm for optimizing the consensus of well-adapted techniques for the inference of gene regulation networks. Building on the methodology of a previous proposal (GENECI), this research adds a local search phase that incorporates prior knowledge about gene interactions, thereby enhancing the optimization process under the influence of domain expert. The algorithm focuses on the evaluation of candidate solutions through a detailed evolutionary process, where known gene interactions guide the evolution of such solutions (individuals). This approach was subjected to rigorous testing using benchmarks from editions 3 and 4 of the DREAM challenges and the yeast network of IRMA, demonstrating a significant improvement in accuracy compared to previous related approaches. The results highlight the effectiveness of the algorithm, even when only 5% of the known interactions are used as a reference. This advancement represents a significant step in the inference of gene regulation networks, providing a more precise and adaptable tool for genomic research.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

next chapter Human Sex Recognition Based on Dimensionality and Uncertainty of Gait Motion Capture Data

Altay, G., Emmert-Streib, F.: Inferring the conservative causal core of gene regulatory networks. BMC Syst. Biol. 4(1), 1–13 (2010)CrossRef

Aluru, M., Shrivastava, H., Chockalingam, S.P., Shivakumar, S., Aluru, S.: Engrain: a supervised ensemble learning method for recovery of large-scale gene regulatory networks. Bioinformatics 38(5), 1312–1319 (2022)CrossRef

Baliarsingh, S.K., Muhammad, K., Bakshi, S.: Sara: a memetic algorithm for high-dimensional biomedical data. Appl. Soft Comput. 101, 107009 (2021)CrossRef

Cantone, I., Marucci, L., et al.: A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell 137(1), 172–181 (2009)CrossRef

Correa, L., Borguesan, B., Farfan, C., Inostroza-Ponta, M., Dorn, M.: A memetic algorithm for 3D protein structure prediction problem. IEEE/ACM Trans. Comput. Biol. Bioinf. 15(3), 690–704 (2016)CrossRef

Escorcia-Rodríguez, J.M., Gaytan-Nuñez, E., et al.: Improving gene regulatory network inference and assessment: the importance of using network structure. Front. Genet. 14, 1143382 (2023)CrossRef

Faith, J.J., Hayete, B., Thaden, J.T., et al.: Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 5(1), e8 (2007)CrossRef

Finkle, J.D., Wu, J., Bagheri, N.: Windowed granger causal inference strategy improves discovery of gene regulatory networks. Proc. Nat. Acad. Sci. 115, 2252–2257 (2018)CrossRef

Fujii, C., Kuwahara, H., Yu, G., et al.: Learning gene regulatory networks from gene expression data using weighted consensus. Neurocomputing 220, 23–33 (2017)CrossRef

10.

Gan, Y., Hu, X., Zou, G., et al.: Inferring gene regulatory networks from single-cell transcriptomic data using bidirectional RNN. Front. Oncol. 12 (2022)

11.

García-Nieto, J., Nebro, A.J., Aldana-Montes, J.F.: Inference of gene regulatory networks with multi-objective cellular genetic algorithm. Comput. Biol. Chem. 80, 409–418 (2019)CrossRef

12.

Ghazikhani, A., Akbarzadeh, T., Monsefi, R.: Genetic regulatory network inference using recurrent neural networks trained by a multi agent system. In: 2011 1st International eConference on Computer and Knowledge Engineering (ICCKE) (2011)

13.

Gong, M., Peng, Z., Ma, L., Huang, J.: Global biological network alignment by using efficient memetic algorithm. IEEE/ACM Trans. Comput. Biol. Bioinf. 13(6), 1117–1129 (2015)CrossRef

14.

Han, P., Gopalakrishnan, C., Yu, H., Wang, E.: Gene regulatory network rewiring in the immune cells associated with cancer. Genes 8(11), 308 (2017)CrossRef

15.

Hillerton, T., et al.: Fast and accurate gene regulatory network inference by normalized least squares regression. Bioinformatics 38(8), 2263–2268 (2022)CrossRef

16.

Hurtado, S., Garcia-Nieto, J., Navas-Delgado, I., Nebro, A.J., Aldana-Montes, J.F.: Reconstruction of gene regulatory networks with multi-objective particle swarm optimisers. Appl. Intell. 51, 1972–1991 (2021)CrossRef

17.

Huynh-Thu, V.A., Irrthum, A., Wehenkel, L., Geurts, P.: Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5(9), e12776 (2010)CrossRef

18.

Jiang, H., Turki, T., Zhang, S., Wang, J.T.L.: Reverse engineering gene regulatory networks using graph mining. In: Perner, P. (ed.) MLDM 2018. LNCS (LNAI), vol. 10934, pp. 335–349. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96136-1_27CrossRef

19.

Khojasteh, H., Khanteymoori, A., Olyaee, M.H.: EnGRNT: inference of gene regulatory networks using ensemble methods and topological feature extraction. Inform. Med. Unlocked 27, 100773 (2021)CrossRef

20.

Kizaki, N., et al.: The inference method of the gene regulatory network with a majority rule. Nonlinear Theory Appl. IEICE 6, 226–236 (2015)CrossRef

21.

de Lima Corrêa, L., Dorn, M.: A multi-population memetic algorithm for the 3-D protein structure prediction problem. Swarm Evol. Comput. 55, 100677 (2020)CrossRef

22.

Liu, L., et al.: Reconstructing gene regulatory networks via memetic algorithm and lasso based on recurrent neural networks. Soft. Comput. 24, 4205–4221 (2020)CrossRef

23.

Liu, W., et al.: Inferring gene regulatory networks using the improved Markov blanket discovery algorithm. Interdisc. Sci. Comput. Life Sci. 1–14 (2022)

24.

Marbach, D., Costello, J.C., Küffner, R., et al.: Wisdom of crowds for robust gene network inference. Nat. Methods 9(8), 796–804 (2012)CrossRef

25.

Margolin, A.A., Nemenman, I., Basso, K., Wiggins, C., et al.: Aracne: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformat. 7, 1–15 (2006). https://doi.org/10.1186/1471-2105-7-S1-S7CrossRef

26.

Meyer, P., Saez-Rodriguez, J.: Advances in systems biology modeling: 10 years of crowdsourcing dream challenges. Cell Syst. 12(6), 636–653 (2021)CrossRef

27.

Moerman, T., et al.: GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks. Bioinformatics 35(12), 2159–2161 (2018)CrossRef

28.

Narasimhan, S., Rengaswamy, R., Vadigepalli, R.: Structural properties of gene regulatory networks: definitions and connections. IEEE/ACM Trans. Comput. Biol. Bioinf. 6(1), 158–170 (2009)CrossRef

29.

Nazarieh, M., Wiese, A., Will, T., Hamed, M., Helms, V.: Identification of key player genes in gene regulatory networks. BMC Syst. Biol. 10, 1–12 (2016)CrossRef

30.

Parikshak, N.N., et al.: Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat. Rev. Genet. 16(8), 441–458 (2015)CrossRef

31.

Peignier, S., Sorin, B., Calevro, F.: Ensemble learning based gene regulatory network inference. In: 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), pp. 113–120 (2021)

32.

Schmitt, P., et al.: GReNaDIne: a data-driven python library to infer gene regulatory networks from gene expression data. Genes 14(2), 269 (2023)CrossRef

33.

Segura-Ortiz, A., García-Nieto, J., et al.: GENECI: a novel evolutionary machine learning consensus-based approach for the inference of gene regulatory networks. Comput. Biol. Med. 155, 106653 (2023)CrossRef

34.

Skok Gibbs, C., et al.: High-performance single-cell gene regulatory network inference at scale: the inferelator 3.0. Bioinformatics 38(9), 2519–2528 (2022)

35.

Watanabe, Y., Seno, S., Takenaka, Y., Matsuda, H.: An estimation method for inference of gene regulatory network using Bayesian network with uniting of partial problems. BMC Genom. 13, S12 (2012)CrossRef

36.

Wijst, M.G.V.D., Vries, D.H.D., Brugge, H., Westra, H.J., Franke, L.: An integrative approach for building personalized gene regulatory networks for precision medicine. Genome Med. 10(1), 1–15 (2018)

37.

Wu, J., Zhao, X., Lin, Z., Shao, Z.: Large scale gene regulatory network inference with a multi-level strategy. Mol. BioSyst. 12, 588–597 (2016)CrossRef

38.

Yang, B., Xu, Y.: Reconstructing gene regulation network based on conditional mutual information. In: Proceedings of the 2017 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2017) (2017)

39.

Yasuki, H., Kikuchi, M., Kurokawa, H.: Inferring method of the gene regulatory networks using neural networks adopting a majority rule. In: The 2011 International Joint Conference on Neural Networks (2011)

40.

Yin, F., Zhou, J., Xie, W., Zhu, Z.: Inferring sparse genetic regulatory networks based on maximum-entropy probability model and multi-objective memetic algorithm. Memetic Comput. 15(1), 117–137 (2023)CrossRef

41.

Zarayeneh, N., et al.: Integration of multi-omics data for integrative gene regulatory network inference. Int. J. Data Min. Bioinform. 18, 223 (2017)CrossRef

42.

Zhao, M., He, W., Tang, J., Zou, Q., Guo, F.: A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data. Briefings Bioinformat. 23(2), bbab568 (2022)

Title: Exploiting Medical-Expert Knowledge Via a Novel Memetic Algorithm for the Inference of Gene Regulatory Networks
Authors: Adrián Segura-Ortiz
José García-Nieto
José F. Aldana-Montes
Publisher: Springer Nature Switzerland
Book: Computational Science – ICCS 2024
Print ISBN: 978-3-031-63771-1

Electronic ISBN: 978-3-031-63772-8

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-63772-8_1

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"