The regulatory role of the Micro-RNAs (miRNAs) in the messenger RNAs (mRNAs) gene expression is well understood by the biologists since some decades, even though the delving into specific aspects is in progress. Clustering is a cornerstone in bioinformatics research, offering a potent computational tool for analyzing diverse types of data encountered in genomics and related fields. MiRNA clustering plays a pivotal role in deciphering the intricate regulatory roles of miRNAs in biological systems. It uncovers novel biomarkers for disease diagnosis and prognosis and advances our understanding of gene regulatory networks and pathways implicated in health and disease, as well as drug discovery. Namely, we have implemented clustering procedure to find interrelations among miRNAs within clusters, and their relations to diseases. Deep clustering (DC) algorithms signify a departure from traditional clustering methods towards more sophisticated techniques, that can uncover intricate patterns and relationships within gene expression data. Deep learning (DL) models have shown remarkable success in various domains, and their application in genomics, especially for tasks like clustering, holding immense promise. The deep convolutional clustering procedure used is different from other traditional methods, demonstrating unbiased clustering results. In the paper, we implement the procedure on a Multiple Myeloma miRNA dataset publicly available on GEO platform, as a template of a cancer instance analysis, and hazard some biological issues.
References
[1]
Dundar, E., Jin, A. and Culurciello, J. (2014) Preprint Repository arXiv Achieves Milestone Million Uploads. Physics Today, 2014, No. 12. https://doi.org/10.1063/pt.5.028530
[2]
Ozgul, O.F., Bardak, B. and Tan, M. (2021) A Convolutional Deep Clustering Framework for Gene Expression Time Series. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18, 2198-2207. https://doi.org/10.1109/tcbb.2020.2988985
[3]
Shommo, G. and Apolloni, B. (2021) A Holistic miRNA-mRNA Module Discovery. Non-Coding RNA Research, 6, 159-166. https://doi.org/10.1016/j.ncrna.2021.09.001
[4]
Karim, M.R., Beyan, O., Zappa, A., Costa, I.G., Rebholz-Schuhmann, D., Cochez, M., et al. (2020) Deep Learning-Based Clustering Approaches for Bioinformatics. Briefings in Bioinformatics, 22, 393-415. https://doi.org/10.1093/bib/bbz170
[5]
Miotto, R., Wang, F., Wang, S., Jiang, X. and Dudley, J.T. (2017) Deep Learning for Healthcare: Review, Opportunities and Challenges. Briefings in Bioinformatics, 19, 1236-1246. https://doi.org/10.1093/bib/bbx044
[6]
Zhou, X., Menche, J., Barabási, A. and Sharma, A. (2014) Human Symptoms-Disease Network. Nature Communications, 5, Article No. 4212. https://doi.org/10.1038/ncomms5212
[7]
An, J., Lai, J., Lehman, M.L. and Nelson, C.C. (2012) Mirdeep*: An Integrated Application Tool for miRNA Identification from RNA Sequencing Data. Nucleic Acids Research, 41, 727-737. https://doi.org/10.1093/nar/gks1187
[8]
LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep Learning. Nature, 521, 436-444. https://doi.org/10.1038/nature14539
[9]
Xie, J., Girshick, R. and Farhadi, A. (2016) Unsupervised Deep Embedding for Clustering Analysis. 33rdInternational Conference on Machine LearningICML 2016, Vol. 1, 740-749.
[10]
Yang, J., Parikh, D. and Batra, D. (2016) Joint Unsupervised Learning of Deep Representations and Image Clusters. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 5147-5156. https://doi.org/10.1109/cvpr.2016.556
[11]
Lukic, Y., Vogt, C., Durr, O. and Stadelmann, T. (2016) Speaker Identification and Clustering Using Convolutional Neural Networks. 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Vietri sul Mare, 13-16 September 2016, 1-6. https://doi.org/10.1109/mlsp.2016.7738816
[12]
Hsu, Y.-C. and Kira, Z. (2015) Neural Network-Based Clustering Using Pairwise Constraints. 1-12. http://arxiv.org/abs/1511.06321
Young, T., Hazarika, D., Poria, S. and Cambria, E. (2018) Recent Trends in Deep Learning Based Natural Language Processing [Review Article]. IEEE Computational Intelligence Magazine, 13, 55-75. https://doi.org/10.1109/mci.2018.2840738
[15]
Guo, X., Liu, X., Zhu, E. and Yin, J. (2017) Deep Clustering with Convolutional Autoencoders. In: Liu, D.R., et al., Eds., Neural Information Processing, Springer International Publishing, 373-382. https://doi.org/10.1007/978-3-319-70096-0_39
[16]
Yang, M., Fu, B., Sidiropoulos, X. and Hong, N.D. (2017) Towards k-Means-Friendly Spaces: Simultaneous Deep Learning and Clustering. International Conference on Machine Learning, Sydney, 6-11 August 2017, 3861-3870.
[17]
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y. and Manzagol, P.A. (2010) Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. Journal of Machine Learning Research, 11, 3371-3408.
[18]
Xue, H., Li, J., Xie, H. and Wang, Y. (2018) Review of Drug Repositioning Approaches and Resources. International Journal of Biological Sciences, 14, 1232-1244. https://doi.org/10.7150/ijbs.24612
[19]
Ren, Y., Pu, J., Yang, Z., Xu, J., Li, G., Pu, X., et al. (2024) Deep Clustering: A Comprehensive Survey. IEEE Transactions on Neural Networks and Learning Systems, 1-21. https://doi.org/10.1109/tnnls.2024.3403155
Marwan, N., Wessel, N., Meyerfeldt, U., Schirdewan, A. and Kurths, J. (2002) Recurrence-Plot-Based Measures of Complexity and Their Application to Heart-Rate-Variability Data. Physical Review E, 66, Article ID: 026702. https://doi.org/10.1103/physreve.66.026702
[22]
Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986) Learning Representations by Back-Propagating Errors. Nature, 323, 533-536. https://doi.org/10.1038/323533a0
[23]
Masci, J., Meier, U., Cireşan, D. and Schmidhuber, J. (2011) Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction. In: Honkela, T., et al., Eds., Artificial Neural Networks and Machine Learning—ICANN 2011, Springer, 52-59. https://doi.org/10.1007/978-3-642-21735-7_7
[24]
Goodfellow, I., Bengio, Y. and Courville, A. (2016) Deep Learning. MIT Press.
[25]
Ben-Dor, A. and Yakhini, Z. (1999) Clustering Gene Expression Patterns. Proceedings of the 3rd Annual International Conference on Computational Molecular Biology, Lyon, 11-14 April 1999, 33-42. https://doi.org/10.1145/299432.299448
[26]
Pairo-Castineira, E., Clohisey, S., Klaric, L., Bretherick, A.D., Rawlik, K., Pasko, D., et al. (2020) Genetic Mechanisms of Critical Illness in Covid-19. Nature, 591, 92-98. https://doi.org/10.1038/s41586-020-03065-y
[27]
Conkrite, K., Sundby, M., Mukai, S., Thomson, J.M., Mu, D., Hammond, S.M., et al. (2011) miR-17∼92 Cooperates with RB Pathway Mutations to Promote Retinoblastoma. Genes & Development, 25, 1734-1745. https://doi.org/10.1101/gad.17027411
[28]
Mohajeri Khorasani, A., Mohammadi, S., Raghibi, A., Haj Mohammad Hassani, B., Bazghandi, B. and Mousavi, P. (2024) miR-17-92a-1 Cluster Host Gene: A Key Regulator in Colorectal Cancer Development and Progression. Clinical and Experimental Medicine, 24, Article No. 85. https://doi.org/10.1007/s10238-024-01331-1
[29]
Galván-Román, J.M., Lancho-Sánchez, Á., Luquero-Bueno, S., Vega-Piris, L., Curbelo, J., Manzaneque-Pradales, M., et al. (2020) Usefulness of Circulating microRNAs miR-146a and miR-16-5p as Prognostic Biomarkers in Community-Acquired Pneumonia. PLOS ONE, 15, e0240926. https://doi.org/10.1371/journal.pone.0240926
[30]
Morsiani, C., Collura, S., Sevini, F., Ciurca, E., Bertuzzo, V.R., Franceschi, C., et al. (2023) Circulating miR-122-5p, miR-92a-3p, and miR-18a-5p as Potential Biomarkers in Human Liver Transplantation Follow-Up. International Journal of Molecular Sciences, 24, Article No. 3457. https://doi.org/10.3390/ijms24043457
[31]
Cao, D., Wang, J., Ji, Z., Shangguan, Y., Guo, W., Feng, X., et al. (2020) Profiling the mRNA and Mirna in Peripheral Blood Mononuclear Cells in Subjects with Active Tuberculosis. Infection and Drug Resistance, 13, 4223-4234. https://doi.org/10.2147/idr.s278705
