Structured Sparse Regularization-Based Deep Fuzzy Networks for RNA N6-Methyladenosine Sites Prediction
Authors: 
Leyao Wang, Yuqing Qian, Hao Xie, Yijie Ding, Fei GuoAuthors Info & Claims
Pages 131 - 144
Abstract
In many biological processes, N6-methyladenosine (m6A) plays a critical role. Experimental methods for identifying m6A sites have proven to be costly, and existing computational methods still require improvement. To address these challenges, we develop a novel computational method called structured sparse regularization-based fuzzy hierarchical echo state network to identify m6A sites in mammals. We apply fuzzy systems to deep learning. Compared with traditional fuzzy inference systems, this deep fuzzy network has the ability to generate feature representations. Echo state network (ESN) is a special type of recurrent neural network, which consists of an input layer, a randomly generated large fixed hidden layer (called a reservoir), and an adaptive output layer. The advantages of our method over ESNs are that it is capable of mining and capturing hidden features layer-by-layer within reservoirs and has better approximation performance. In order to remove redundancy, the output layer weights are trained by structured sparse learning, which enhances the generalizability and robustness of the method. Evaluation of our method by testing it on tissue-specific datasets shows that it outperforms existing tools.
References
[1]
H. Shi, J. Wei, and C. He, “Where, when, and how: Context-dependent functions of RNA methylation writers, readers, and erasers,” Mol. Cell, vol. 74, no. 4, pp. 640–650, 2019.
[2]
X. Wang et al., “N6-methyladenosine modulates messenger RNA translation efficiency,” Cell, vol. 161, no. 6, pp. 1388–1399, 2015.
[3]
P. J. Batista et al., “m6A RNA modification controls cell fate transition in mammalian embryonic stem cells,” Cell Stem Cell, vol. 15, no. 6, pp. 707–719, 2014.
[4]
X. Jiang et al., “The role of m6A modification in the biological functions and diseases,” Signal Transduct. Target. Ther., vol. 6, no. 1, 2021, Art. no.
[5]
G. Jia, Y. Fu, and C. He, “Reversible RNA adenosine methylation in biological regulation,” Trends Genet., vol. 29, no. 2, pp. 108–115, 2013.
[6]
J. Liu et al., “A METTL3–METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation,” Nature Chem. Biol., vol. 10, no. 2, pp. 93–95, 2014.
[7]
G. Jia et al., “N6-methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO,” Nature Chem. Biol., vol. 7, no. 12, pp. 885–887, 2011.
[8]
K. D. Meyer, Y. Saletore, P. Zumbo, O. Elemento, C. E. Mason, and S. R. Jaffrey, “Comprehensive analysis of mRNA methylation reveals enrichment in 3’ UTRs and near stop codons,” Cell, vol. 149, no. 7, pp. 1635–1646, 2012.
[9]
D. Dominissini et al., “Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq,” Nature, vol. 485, no. 7397, pp. 201–206, 2012.
[10]
B. Linder, A. V. Grozhik, A. O. O.-George, C. Meydan, C. E. Mason, and S. R. Jaffrey, “Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome,” Nature Methods, vol. 12, no. 8, pp. 767–772, 2015.
[11]
Z. Zhang et al., “Single-base mapping of m6A by an antibody-independent method,” Sci. Adv., vol. 5, no. 7, 2019, Art. no.
[12]
F.-Y. Dao, H. Lv, Y.-H. Yang, H. Zulfiqar, H. Gao, and H. Lin, “Computational identification of n6-methyladenosine sites in multiple tissues of mammals,” Comput. Struct. Biotechnol. J., vol. 18, pp. 1084–1091, 2020.
[13]
K. Liu, L. Cao, P. Du, and W. Chen, “im6A-TS-CNN: Identifying the n6-methyladenine site in multiple tissues by using the convolutional neural network,” Mol. Ther.-Nucleic Acids, vol. 21, pp. 1044–1049, 2020.
[14]
Z. Abbas, H. Tayara, Q. Zou, and K. T. Chong, “TS-m6A-DL: Tissue-specific identification of n6-methyladenosine sites using a universal deep learning model,” Comput. Struct. Biotechnol. J., vol. 19, pp. 4619–4625, 2021.
[15]
Z. Luo, L. Lou, W. Qiu, Z. Xu, and X. Xiao, “Predicting n6-methyladenosine sites in multiple tissues of mammals through ensemble deep learning,” Int. J. Mol. Sci., vol. 23, no. 24, 2022, Art. no.
[16]
M. U. Rehman, H. Tayara, and K. T. Chong, “DL-m6A: Identification of N6-methyladenosine sites in mammals using deep learning based on different encoding schemes,” IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 20, no. 2, pp. 904–911, Mar./Apr. 2023.
[17]
Y. Fan, G. Sun, and X. Pan, “ELMo4m6A: A contextual language embedding-based predictor for detecting RNA N6-methyladenosine sites,” IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 20, no. 2, pp. 944–954, Mar./Apr. 2023.
[18]
C. Hendra et al., “Detection of m6a from direct RNA sequencing using a multiple instance learning framework,” Nature Methods, vol. 19, no. 12, pp. 1590–1598, 2022.
[19]
L. Wang, P. Tiwari, Y. Ding, and F. Guo, “Weighted fuzzy system for identifying DNA N4-methylcytosine sites with kernel entropy component analysis,” IEEE Trans. Artif. Intell., vol. 5, no. 2, pp. 895–903, Feb. 2024.
[20]
X. Yu, J. Hu, and Y. Zhang, “SNN6mA: Improved DNA N6-methyladenine site prediction using siamese network-based feature embedding,” Comput. Biol. Med., vol. 166, 2023, Art. no.
[21]
T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Trans. Syst., Man, Cybern., vol. SMC-15, no. 1, pp. 116–132, Jan./Feb. 1985.
[22]
Y. Ding, P. Tiwari, Q. Zou, F. Guo, and H. Pandey, “C-loss based higher-order fuzzy inference systems for identifying DNA N4-methylcytosine sites,” IEEE Trans. Fuzzy Syst., vol. 30, no. 11, pp. 4754–4765, Nov. 2022.
[23]
Q. Zhou, P. Shi, H. Liu, and S. Xu, “Neural-network-based decentralized adaptive output-feedback control for large-scale stochastic nonlinear systems,” IEEE Trans. Syst., Man, Cybern., Part B (Cybern.), vol. 42, no. 6, pp. 1608–1619, Dec. 2012.
[24]
C.-C. Ku and K. Y. Lee, “Diagonal recurrent neural networks for dynamic systems control,” IEEE Trans. Neural Netw., vol. 6, no. 1, pp. 144–156, Jan. 1995.
[25]
H. Jaeger, “The ‘echo state’ approach to analysing and training recurrent neural networks-with an erratum note,” in German National Research Center for Information Technology GMD, Bonn, Germany: Tech. Rep. 148, 2001.
[26]
X. Sun, T. Li, Q. Li, Y. Huang, and Y. Li, “Deep belief echo-state network and its application to time series prediction,” Knowl.-Based Syst., vol. 130, pp. 17–29, 2017.
[27]
Z. K. Malik, A. Hussain, and Q. J. Wu, “Multilayered echo state machine: A novel architecture and algorithm,” IEEE Trans. Cybern., vol. 47, no. 4, pp. 946–959, Apr. 2017.
[28]
C. Gallicchio, A. Micheli, and L. Pedrelli, “Design of deep echo state networks,” Neural Netw., vol. 108, pp. 33–47, 2018.
[29]
X. Na, W. Ren, M. Liu, and M. Han, “Hierarchical echo state network with sparse learning: A method for multidimensional chaotic time series prediction,” IEEE Trans. Neural Netw. Learn. Syst., vol. 34, no. 11, pp. 9302–9313, Nov. 2023.
[30]
Y. Zhang, H. Ishibuchi, and S. Wang, “Deep Takagi-Sugeno-Kang fuzzy classifier with shared linguistic fuzzy rules,” IEEE Trans. Fuzzy Syst., vol. 26, no. 3, pp. 1535–1549, Jun. 2018.
[31]
N. Giang et al., “Novel incremental algorithms for attribute reduction from dynamic decision tables using hybrid filter–wrapper with fuzzy partition distance,” IEEE Trans. Fuzzy Syst., vol. 28, no. 5, pp. 858–873, May 2020.
[32]
J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: The fuzzy c-means clustering algorithm,” Comput. Geosci., vol. 10, no. 2/3, pp. 191–203, 1984.
[33]
Z. Deng, Y. Jiang, K. Choi, F. Chung, and S. Wang, “Knowledge-leverage-based TSK fuzzy system modeling,” IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 8, pp. 1200–1212, Aug. 2013.
[34]
X. Guo, P. Tiwari, Q. Zou, and Y. Ding, “Subspace projection-based weighted echo state networks for predicting therapeutic peptides,” Knowl.-Based Syst., vol. 263, 2023, Art. no.
[35]
Q. Wang, Y. Pan, J. Cao, and H. Liu, “Adaptive fuzzy echo state network control of fractional-order large-scale nonlinear systems with time-varying deferred constraints,” IEEE Trans. Fuzzy Syst., vol. 32, no. 2, pp. 634–648, Feb. 2024.
[36]
H. Wang et al., “Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning,” Bioinformatics, vol. 28, no. 12, pp. i127–i136, 2012.
[37]
J. Wang et al., “Manifold-regularized multitask fuzzy system modeling with low-rank and sparse structures in consequent parameters,” IEEE Trans. Fuzzy Syst., vol. 30, no. 5, pp. 1486–1500, May 2022.
[38]
J. Zhou, W. Pedrycz, C. Gao, Z. Lai, J. Wan, and Z. Ming, “Robust jointly sparse fuzzy clustering with neighborhood structure preservation,” IEEE Trans. Fuzzy Syst., vol. 30, no. 4, pp. 1073–1087, Apr. 2022.
[39]
L. Wang and S. Chen, “$l\_\lbrace 2, p\rbrace$ matrix norm and its application in feature selection,” 2013, arXiv:1303.3987.
[40]
Y. Jiao and P. Du, “Performance measures in evaluating machine learning based bioinformatics predictors for classifications,” Quantitative Biol., vol. 4, pp. 320–330, 2016.
[41]
M. Tahir, H. Tayara, and K. T. Chong, “iPseU-CNN: Identifying RNA pseudouridine sites using convolutional neural networks,” Mol. Ther.-Nucleic Acids, vol. 16, pp. 463–470, 2019.
[42]
D. Zhang et al., “iCarPS: A computational tool for identifying protein carbonylation sites by novel encoded features,” Bioinformatics, vol. 37, no. 2, pp. 171–177, 2021.
[43]
J. A. Hanley and B. J. McNeil, “The meaning and use of the area under a receiver operating characteristic ( ROC) curve,” Radiol., vol. 143, no. 1, pp. 29–36, 1982.
[44]
Y. Zhou, P. Zeng, Y. H. Li, Z. Zhang, and Q. Cui, “SRAMP: Prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features,” Nucleic Acids Res., vol. 44, no. 10, 2016, Art. no.
[45]
H. Lin, E. Deng, H. Ding, W. Chen, and K. Chou, “iPro54-PseKNC: A sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition,” Nucleic Acids Res., vol. 42, no. 21, pp. 12 961–12 972, 2014.
[46]
Y. Wang, Y. Zhai, Y. Ding, and Q. Zou, “SBSM-pro: Support bio-sequence machine for proteins,” 2023, arXiv:2308.10275.
[47]
W. He, C. Jia, and Q. Zou, “4mCPred: Machine learning methods for DNA N4-methylcytosine sites prediction,” Bioinformatics, vol. 35, no. 4, pp. 593–601, 2019.
[48]
Y. Zhang et al., “Leveraging the attention mechanism to improve the identification of DNA N6-methyladenine sites,” Brief. Bioinf., vol. 22, no. 6, 2021, Art. no.
[49]
V. Vacic, L. M. Iakoucheva, and P. Radivojac, “Two sample logo: A graphical representation of the differences between two sets of sequence alignments,” Bioinformatics, vol. 22, no. 12, pp. 1536–1537, 2006.
Index Terms
- Structured Sparse Regularization-Based Deep Fuzzy Networks for RNA N6-Methyladenosine Sites Prediction
Index terms have been assigned to the content through auto-classification.
Recommendations
Transformed ℓ 1 regularization for learning sparse deep neural networks
AbstractDeep Neural Networks (DNNs) have achieved extraordinary success in numerous areas. However, DNNs often carry a large number of weight parameters, leading to the challenge of heavy memory and computation costs. Overfitting is another ...
Sparse Learning for Neural Networks with A Generalized Sparse Regularization
AbstractDeep neural networks (DNNs) is very important and have achieved remarkable accuracies in tasks such as image processing. However, the success of DNNs heavily relies on excessive computation and parameter storage costs. To cut down the overheads, a ...
Group sparse regularization for deep neural networks
In this paper, we address the challenging task of simultaneously optimizing (i) the weights of a neural network, (ii) the number of neurons for each hidden layer, and (iii) the subset of active input features (i.e., feature selection). While these ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
1941-0034 © 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
Publisher
IEEE Press
Publication History
Published: 01 January 2025
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 23 Jan 2025