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Current Genomics

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ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

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Research Article

DLC-ac4C: A Prediction Model for N4-acetylcytidine Sites in Human mRNA Based on DenseNet and Bidirectional LSTM Methods

Author(s): Jianhua Jia*, Xiaojing Cao and Zhangying Wei

Volume 24, Issue 3, 2023

Published on: 19 October, 2023

Page: [171 - 186] Pages: 16

DOI: 10.2174/0113892029270191231013111911

open access plus

Abstract

Introduction: N4 acetylcytidine (ac4C) is a highly conserved nucleoside modification that is essential for the regulation of immune functions in organisms. Currently, the identification of ac4C is primarily achieved using biological methods, which can be time-consuming and laborintensive. In contrast, accurate identification of ac4C by computational methods has become a more effective method for classification and prediction.

Aim: To the best of our knowledge, although there are several computational methods for ac4C locus prediction, the performance of the models they constructed is poor, and the network structure they used is relatively simple and suffers from the disadvantage of network degradation. This study aims to improve these limitations by proposing a predictive model based on integrated deep learning to better help identify ac4C sites.

Methods: In this study, we propose a new integrated deep learning prediction framework, DLCac4C. First, we encode RNA sequences based on three feature encoding schemes, namely C2 encoding, nucleotide chemical property (NCP) encoding, and nucleotide density (ND) encoding. Second, one-dimensional convolutional layers and densely connected convolutional networks (DenseNet) are used to learn local features, and bi-directional long short-term memory networks (Bi-LSTM) are used to learn global features. Third, a channel attention mechanism is introduced to determine the importance of sequence characteristics. Finally, a homomorphic integration strategy is used to limit the generalization error of the model, which further improves the performance of the model.

Results: The DLC-ac4C model performed well in terms of sensitivity (Sn), specificity (Sp), accuracy (Acc), Mathews correlation coefficient (MCC), and area under the curve (AUC) for the independent test data with 86.23%, 79.71%, 82.97%, 66.08%, and 90.42%, respectively, which was significantly better than the prediction accuracy of the existing methods.

Conclusion: Our model not only combines DenseNet and Bi-LSTM, but also uses the channel attention mechanism to better capture hidden information features from a sequence perspective, and can identify ac4C sites more effectively.

Keywords: N4-acetylcytidine site prediction, DenseNet, Bi-LSTM, channel attention mechanism, deep learning, ensemble learning.

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[1]
Boccaletto, P.; Machnicka, M.A.; Purta, E.; Piatkowski, P.; Baginski, B.; Wirecki, T.K.; de Crecy-Lagard, V.; Ross, R.; Limbach, P.A.; Kotter, A.; Helm, M.; Bujnicki, J.M. MODOMICS: A database of RNA modification pathways. 2017 update. Nucleic Acids Res., 2018, 46(D1), D303-D307.
[2]
Chen, L.; Wang, W.J.; Liu, Q.; Wu, Y.K.; Wu, Y.W.; Jiang, Y.; Liao, X.Q.; Huang, F.; Li, Y.; Shen, L.; Yu, C.; Zhang, S.Y.; Yan, L.Y.; Qiao, J.; Sha, Q.Q.; Fan, H.Y. NAT10-mediated N 4-acetylcytidine modification is required for meiosis entry and progression in male germ cells. Nucleic Acids Res., 2022, 50(19), 10896-10913.
[http://dx.doi.org/10.1093/nar/gkac594] [PMID: 35801907]
[3]
Cui, Z.; Xu, Y.; Wu, P.; Lu, Y.; Tao, Y.; Zhou, C.; Cui, R.; Li, J.; Han, R. NAT10 promotes osteogenic differentiation of periodontal ligament stem cells by regulating VEGFA-mediated PI3K/AKT signaling pathway through ac4C modification. Odontology, 2023, 111(4), 870-882.
[http://dx.doi.org/10.1007/s10266-023-00793-1] [PMID: 36879181]
[4]
Wang, G.; Zhang, M.; Zhang, Y.; Xie, Y.; Zou, J.; Zhong, J.; Zheng, Z.; Zhou, X.; Zheng, Y.; Chen, B.; Liu, C. NAT10‐mediated mRNA N4‐acetylcytidine modification promotes bladder cancer progression. Clin. Transl. Med., 2022, 12(5), e738.
[http://dx.doi.org/10.1002/ctm2.738] [PMID: 35522942]
[5]
Kawai, G.; Hashizume, T.; Miyazawa, T.; McCloskey, J.A.; Yokoyama, S.J.N.a.s.s Conformational characteristics of 4-acetylcytidine found in tRNA. Nucleic Acids Symp. Ser., 1989, 1989M(21), 61-62.
[6]
Kumbhar, B.V.; Kamble, A.D.; Sonawane, K.D. Conformational preferences of modified nucleoside N(4)-acetylcytidine, ac4C occur at “wobble” 34th position in the anticodon loop of tRNA. Cell Biochem. Biophys., 2013, 66(3), 797-816.
[http://dx.doi.org/10.1007/s12013-013-9525-8] [PMID: 23408308]
[7]
Orita, I.; Futatsuishi, R.; Adachi, K.; Ohira, T.; Kaneko, A.; Minowa, K.; Suzuki, M.; Tamura, T.; Nakamura, S.; Imanaka, T.; Suzuki, T.; Fukui, T. Random mutagenesis of a hyperthermophilic archaeon identified tRNA modifications associated with cellular hyperthermotolerance. Nucleic Acids Res., 2019, 47(4), 1964-1976.
[http://dx.doi.org/10.1093/nar/gky1313] [PMID: 30605516]
[8]
Bruenger, E.; Kowalak, J.A.; Kuchino, Y.; McCloskey, J.A.; Mizushima, H.; Stetter, K.O.; Crain, P.F. 5S rRNA modification in the hyperthermophilic archaea Sulfolobus solfataricus and Pyrodictium occultum. FASEB J., 1993, 7(1), 196-200.
[http://dx.doi.org/10.1096/fasebj.7.1.8422966] [PMID: 8422966]
[9]
(a) Arango, D.; Sturgill, D.; Alhusaini, N.; Dillman, A.A.; Sweet, T. J.; Hanson, G.; Hosogane, M.; Sinclair, W. R.; Nanan, K. K.; Mandler, M. D.; Fox, S. D.; Zengeya, T. T.; Andresson, T.; Meier, J. L.; Coller, J.; Oberdoerffer, S. Acetylation of cytidine in mRNA promotes translation efficiency. Cell 2018, 175(7), 1872-1886 e1824.;
(b) Tsai, K.; Jaguva Vasudevan, A.A.; Martinez Campos, C.; Emery, A.; Swanstrom, R.; Cullen, B.R. Acetylation of cytidine residues boosts HIV-1 gene expression by increasing viral RNA stability. Cell Host Microbe, 2020, 28(2), 306-312.e6.
[http://dx.doi.org/10.1016/j.chom.2020.05.011] [PMID: 32533923]
[10]
Nance, K.D.; Gamage, S.T.; Alam, M.M.; Yang, A.; Levy, M.J.; Link, C.N.; Florens, L.; Washburn, M.P.; Gu, S.; Oppenheim, J.J.; Meier, J.L. Cytidine acetylation yields a hypoinflammatory synthetic messenger RNA. Cell Chem. Biol., 2022, 29(2), 312-320.e7.
[http://dx.doi.org/10.1016/j.chembiol.2021.07.003] [PMID: 35180432]
[11]
Yang, W.; Li, H.Y.; Wu, Y.F.; Mi, R.J.; Liu, W.Z.; Shen, X.; Lu, Y.X.; Jiang, Y.H.; Ma, M.J.; Shen, H.Y. ac4C acetylation of RUNX2 catalyzed by NAT10 spurs osteogenesis of BMSCs and prevents ovariectomy-induced bone loss. Mol. Ther. Nucleic Acids, 2021, 26, 135-147.
[12]
Law, K.P.; Han, T.L.; Mao, X.; Zhang, H. Tryptophan and purine metabolites are consistently upregulated in the urinary metabolome of patients diagnosed with gestational diabetes mellitus throughout pregnancy: A longitudinal metabolomics study of Chinese pregnant women part 2. Clin. Chim. Acta, 2017, 468, 126-139.
[http://dx.doi.org/10.1016/j.cca.2017.02.018] [PMID: 28238935]
[13]
Feng, Z.; Li, K.; Qin, K.; Liang, J.; Shi, M.; Ma, Y.; Zhao, S.; Liang, H.; Han, D.; Shen, B.; Peng, C.; Chen, H.; Jiang, L. The LINC00623/NAT10 signaling axis promotes pancreatic cancer progression by remodeling ac4C modification of mRNA. J. Hematol. Oncol., 2022, 15(1), 112.
[14]
Jin, G.; Xu, M.; Zou, M.; Duan, S. The processing, gene regulation, biological functions, and clinical relevance of N4-acetylcytidine on RNA: A systematic review. Mol. Ther. Nucleic Acids, 2020, 20, 13-24.
[http://dx.doi.org/10.1016/j.omtn.2020.01.037] [PMID: 32171170]
[15]
Ito, S.; Akamatsu, Y.; Noma, A.; Kimura, S.; Miyauchi, K.; Ikeuchi, Y.; Suzuki, T.; Suzuki, T. A single acetylation of 18 S rRNA is essential for biogenesis of the small ribosomal subunit in Saccharomyces cerevisiae. J. Biol. Chem., 2014, 289(38), 26201-26212.
[http://dx.doi.org/10.1074/jbc.M114.593996] [PMID: 25086048]
[16]
Sharma, S.; Marchand, V.; Motorin, Y.; Lafontaine, D.A-O. Identification of sites of 2′-O-methylation vulnerability in human ribosomal RNAs by systematic mapping. Sci. Rep., 2017, 7(1), 11490.
[17]
Zhou, Y.; Zeng, P.; Li, Y.H.; Zhang, Z.; Cui, Q. SRAMP: Prediction of mammalian N 6 -methyladenosine (m 6 A) sites based on sequence-derived features. Nucleic Acids Res., 2016, 44(10), e91.
[http://dx.doi.org/10.1093/nar/gkw104] [PMID: 26896799]
[18]
Basith, S.; Manavalan, B.; Shin, T.H.; Lee, G. SDM6A: A web-based integrative machine-learning framework for predicting 6mA sites in the rice genome. Mol. Ther. Nucleic Acids, 2019, 18, 131-141.
[http://dx.doi.org/10.1016/j.omtn.2019.08.011] [PMID: 31542696]
[19]
Lv, H.; Zhang, Z.M.; Li, S.H.; Tan, J.X.; Chen, W.; Lin, H. Evaluation of different computational methods on 5-methylcytosine sites identification. Brief. Bioinform., 2020, 21(3), 982-995.
[http://dx.doi.org/10.1093/bib/bbz048] [PMID: 31157855]
[20]
Hasan, M.M.; Basith, S.; Khatun, M.S.; Lee, G.; Manavalan, B.; Kurata, H. Meta-i6mA: An interspecies predictor for identifying DNA N 6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework. Brief. Bioinform., 2021, 22(3), bbaa202.
[http://dx.doi.org/10.1093/bib/bbaa202] [PMID: 32910169]
[21]
Zhao, W.; Zhou, Y.; Cui, Q.; Zhou, Y. PACES: Prediction of N4-acetylcytidine (ac4C) modification sites in mRNA. Sci. Rep., 2019, 9(1), 11112.
[http://dx.doi.org/10.1038/s41598-019-47594-7] [PMID: 31366994]
[22]
Alam, W.; Tayara, H.; Chong, K.T. XG-ac4C: Identification of N4-acetylcytidine (ac4C) in mRNA using eXtreme gradient boosting with electron-ion interaction pseudopotentials. Sci. Rep., 2020, 10(1), 20942.
[http://dx.doi.org/10.1038/s41598-020-77824-2] [PMID: 33262392]
[23]
Su, W.; Xie, X.Q.; Liu, X.W.; Gao, D.; Ma, C.Y.; Zulfiqar, H.; Yang, H.; Lin, H.; Yu, X.L.; Li, Y.W. iRNA-ac4C: A novel computational method for effectively detecting N4-acetylcytidine sites in human mRNA. Int. J. Biol. Macromol., 2023, 227, 1174-1181.
[http://dx.doi.org/10.1016/j.ijbiomac.2022.11.299] [PMID: 36470433]
[24]
Onesime, M.; Yang, Z.; Dai, Q.A.O. Genomic island prediction via chi-square test and random forest algorithm. Comput. Math. Methods Med., 2021, 2021, 9969751.
[25]
Yang, J.; Peng, S.A.O.; Zhang, B.; Houten, S.; Schadt, E.; Zhu, J.; Suh, Y.; Tu, Z.A.O. Human geroprotector discovery by targeting the converging subnetworks of aging and age-related diseases. Geroscience, 2020, 42(1), 353-372.
[26]
Ma, X.; Xi, B.; Zhang, Y.; Zhu, L.; Sui, X.; Tian, G.; Yang, J.J.C.B. A machine learning-based diagnosis of thyroid cancer using thyroid nodules ultrasound images. Curr. Bioinform., 2020, 15(4), 349-358.
[http://dx.doi.org/10.2174/1574893614666191017091959]
[27]
Wang, Y.; Xu, Y.; Yang, Z.; Liu, X.; Dai, Q.A.O. Using recursive feature selection with random forest to improve protein structural class prediction for low-similarity sequences. Comput. Math. Methods Med., 2021, 2021, 5529389.
[28]
Yoo, P.; Zhou, B.; Zomaya, A. Machine learning techniques for protein secondary structure prediction: An overview and evaluation. Curr. Bioinform., 2008, 3(2), 74-86.
[http://dx.doi.org/10.2174/157489308784340676]
[29]
Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S.A.A.; Ballard, A.J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A.W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596(7873), 583-589.
[http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]
[30]
Kang, S.; Li, Q.; Chen, Q.; Zhou, Y.; Park, S.; Lee, G.; Grimes, B.; Krysan, K.; Yu, M.; Wang, W.; Alber, F.; Sun, F.; Dubinett, S.M.; Li, W.; Zhou, X.J. CancerLocator: Non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA. Genome Biol., 2017, 18(1), 53.
[31]
Liu, H.; Qiu, C.; Wang, B.; Bing, P.; Tian, G.; Zhang, X.; Ma, J.; He, B.; Yang, J.; Evaluating, DNA. Evaluating DNA methylation, gene expression, somatic mutation, and their combinations in inferring tumor tissue-of-origin. Front. Cell Dev. Biol., 2021, 9, 619330.
[32]
Liu, Q.; Chen, J.; Wang, Y.; Li, S.; Jia, C.; Song, J.; Li, F. DeepTorrent: A deep learning-based approach for predicting DNA N4-methylcytosine sites. Brief. Bioinform., 2021, 22(3), bbaa124.
[http://dx.doi.org/10.1093/bib/bbaa124]
[33]
Tsukiyama, S.A.O.; Hasan, M.A.O.; Deng, H.W.; Kurata, H.A.O. BERT6mA: Prediction of DNA N6-methyladenine site using deep learning-based approaches. Brief. Bioinform., 2022, 23(2), bbac053.
[http://dx.doi.org/10.1093/bib/bbac053]
[34]
Yu, H.; Dai, Z. SNNRice6mA: A deep learning method for predicting DNA N6-methyladenine sites in rice genome. Front. Genet., 2019, 10, 1071.
[35]
Yang, S.; Yang, Z.; Yang, J. 4mCBERT: A computing tool for the identification of DNA N4-methylcytosine sites by sequence- and chemical-derived information based on ensemble learning strategies. Int. J. Biol. Macromol., 2023, 231, 123180.
[http://dx.doi.org/10.1016/j.ijbiomac.2023.123180] [PMID: 36646347]
[36]
Hasan, M.M.; Tsukiyama, S.; Cho, J.Y.; Kurata, H.; Alam, M.A.; Liu, X.; Manavalan, B.; Deng, H.W. Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy. Mol. Ther., 2022, 30(8), 2856-2867.
[http://dx.doi.org/10.1016/j.ymthe.2022.05.001] [PMID: 35526094]
[37]
Rehman, M.U.; Tayara, H.; Chong, K.T. DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species. Comput. Struct. Biotechnol. J., 2021, 19, 6009-6019.
[http://dx.doi.org/10.1016/j.csbj.2021.10.034] [PMID: 34849205]
[38]
Zhang, G.; Luo, W.; Lyu, J.; Yu, Z.G.; Huang, G. CNNLSTMac4CPred: A hybrid model for N4-acetylcytidine prediction. Interdiscip. Sci., 2022, 14(2), 439-451.
[http://dx.doi.org/10.1007/s12539-021-00500-0] [PMID: 35106702]
[39]
Wang, C.; Ju, Y.; Zou, Q.; Lin, C. DeepAc4C: A convolutional neural network model with hybrid features composed of physicochemical patterns and distributed representation information for identification of N4-acetylcytidine in mRNA. Bioinformatics, 2021, 38(1), 52-57.
[http://dx.doi.org/10.1093/bioinformatics/btab611] [PMID: 34427581]
[40]
Khan, A.; Rehman, H.U.; Habib, U.; Ijaz, U. Detecting N6-methyladenosine sites from RNA transcriptomes using random forest. J. Comput. Sci., 2020, 47, 101238.
[http://dx.doi.org/10.1016/j.jocs.2020.101238]
[41]
Islam, N.; Park, J. bCNN-Methylpred: Feature-Based Prediction of RNA Sequence Modification Using Branch Convolutional Neural Network. Genes, 2021, 12(8), 1155.
[http://dx.doi.org/10.3390/genes12081155] [PMID: 34440330]
[42]
Wei, C.; Zhang, J.; Yuan, X. Enhancing the prediction of protein coding regions in biological sequence via a deep learning framework with hybrid encoding. Digit. Signal Process., 2022, 123, 103430.
[http://dx.doi.org/10.1016/j.dsp.2022.103430]
[43]
Luo, Z.; Su, W.; Lou, L.; Qiu, W.; Xiao, X.; Xu, Z. DLm6Am: A deep-learning-based tool for identifying n6,2′-o-dimethyladenosine sites in RNA sequences. Int. J. Mol. Sci., 2022, 23(19), 11026.
[http://dx.doi.org/10.3390/ijms231911026] [PMID: 36232325]
[44]
Jia, J.; Qin, L.; Lei, R. DGA-5mC: A 5-methylcytosine site prediction model based on an improved DenseNet and bidirectional GRU method. Math. Biosci. Eng., 2023, 20(6), 9759-9780.
[http://dx.doi.org/10.3934/mbe.2023428] [PMID: 37322910]
[45]
Fu, L.; Niu, B.; Zhu, Z.; Wu, S.; Li, W. CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics, 2012, 28(23), 3150-3152.
[http://dx.doi.org/10.1093/bioinformatics/bts565] [PMID: 23060610]
[46]
Xiong, Y.; He, X.; Zhao, D.; Tian, T.; Hong, L.; Jiang, T.; Zeng, J. Modeling multi-species RNA modification through multi-task curriculum learning. Nucleic Acids Res., 2021, 49(7), 3719-3734.
[http://dx.doi.org/10.1093/nar/gkab124] [PMID: 33744973]
[47]
Chen, Z.; Zhao, P.; Li, F.; Marquez-Lago, T.T.; Leier, A.; Revote, J.; Zhu, Y.; Powell, D.R.; Akutsu, T.; Webb, G.I.; Chou, K.C.; Smith, A.I.; Daly, R.J.; Li, J.; Song, J. iLearn: An integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data. Brief. Bioinform., 2020, 21(3), 1047-1057.
[48]
Nguyen-Vo, T.H.; Nguyen, Q.H.; Do, T.T.T.; Nguyen, T.N.; Rahardja, S.; Nguyen, B.P. iPseU-NCP: Identifying RNA pseudouridine sites using random forest and NCP-encoded features. BMC Genomics, 2019, 20(S10), 971.
[http://dx.doi.org/10.1186/s12864-019-6357-y] [PMID: 31888464]
[49]
Dao, F.Y.; Lv, H.; Yang, Y.H.; Zulfiqar, H.; Gao, H.; Lin, H. Computational identification of N6-methyladenosine sites in multiple tissues of mammals. Comput. Struct. Biotechnol. J., 2020, 18, 1084-1091.
[http://dx.doi.org/10.1016/j.csbj.2020.04.015] [PMID: 32435427]
[50]
Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chou, K.C. iRNA-3typeA: Identifying three types of modification at RNA’s adenosine sites. Mol. Ther. Nucleic Acids, 2018, 11, 468-474.
[http://dx.doi.org/10.1016/j.omtn.2018.03.012] [PMID: 29858081]
[51]
Chen, K.; Wei, Z.; Zhang, Q.; Wu, X.; Rong, R.; Lu, Z.; Su, J.; de Magalhães, J.P.; Rigden, D.J.; Meng, J. WHISTLE: A high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach. Nucleic Acids Res., 2019, 47(7), e41.
[http://dx.doi.org/10.1093/nar/gkz074] [PMID: 30993345]
[52]
Jia, J.; Wu, G.; Li, M.; Qiu, W. pSuc-EDBAM: Predicting lysine succinylation sites in proteins based on ensemble dense blocks and an attention module. BMC Bioinformatics, 2022, 23(1), 450.
[http://dx.doi.org/10.1186/s12859-022-05001-5] [PMID: 36316638]
[53]
Cheng, X.; Wang, J.; Li, Q.; Liu, T. BiLSTM-5mC: A bidirectional long short-term memory-based approach for predicting 5-methylcytosine sites in genome-wide DNA promoters. Molecules, 2021, 26(24), 7414.
[http://dx.doi.org/10.3390/molecules26247414] [PMID: 34946497]
[54]
Wang, H.; Yan, Z.; Liu, D.; Zhao, H.; Zhao, J. MDC-Kace: A model for predicting lysine acetylation sites based on modular densely connected convolutional networks. IEEE Access, 2020, 8, 214469-214480.
[http://dx.doi.org/10.1109/ACCESS.2020.3041044]
[55]
Tang, X.; Zheng, P.; Li, X.; Wu, H.; Wei, D.Q.; Liu, Y.; Huang, G. Deep6mAPred: A CNN and Bi-LSTM-based deep learning method for predicting DNA N6-methyladenosine sites across plant species. Methods, 2022, 204, 142-150.
[http://dx.doi.org/10.1016/j.ymeth.2022.04.011] [PMID: 35477057]
[56]
Wang, X.; Ding, Z.; Wang, R.; Lin, X. Deepro-Glu: Combination of convolutional neural network and Bi-LSTM models using ProtBert and handcrafted features to identify lysine glutarylation sites. Brief. Bioinform., 2023, 24(2), bbac631.
[http://dx.doi.org/10.1093/bib/bbac631] [PMID: 36653898]
[57]
Yu, Y.; Si, X.; Hu, C.; Zhang, J. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput., 2019, 31(7), 1235-1270.
[http://dx.doi.org/10.1162/neco_a_01199] [PMID: 31113301]
[58]
Chen, L.; Zhang, H.; Xiao, J.; Nie, L.; Shao, J.; Liu, W.; Chua, T-S. SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning; IEEE, 2017, pp. 1063-6919.
[59]
Jia, J.; Lei, R.; Qin, L.; Wu, G.; Wei, X. iEnhancer-DCSV: Predicting enhancers and their strength based on DenseNet and improved convolutional block attention module. Front. Genet., 2023, 14, 1132018.
[http://dx.doi.org/10.3389/fgene.2023.1132018] [PMID: 36936423]
[60]
Liu, B.; Wang, S.; Long, R.; Chou, K.C. iRSpot-EL: identify recombination spots with an ensemble learning approach. Bioinformatics, 2017, 33(1), 35-41.
[http://dx.doi.org/10.1093/bioinformatics/btw539] [PMID: 27531102]
[61]
Kingma, D.P.; Ba, J.J.C. Adam: A method for stochastic optimization.arXiv:1412.6980, 2014.
[62]
Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R.J.J.o.M.L.R. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res., 2014, 15(1), 1929-1958.
[63]
Yao, Y.; Rosasco, L.; Caponnetto, A. On early stopping in gradient descent learning. Constr. Approx., 2007, 26(2), 289-315.
[http://dx.doi.org/10.1007/s00365-006-0663-2]
[64]
Nahm, F.S. Receiver operating characteristic curve: Overview and practical use for clinicians. Korean J. Anesthesiol., 2022, 75(1), 25-36.
[http://dx.doi.org/10.4097/kja.21209] [PMID: 35124947]
[65]
Liu, Y.; Li, A.; Zhao, X.M.; Wang, M. DeepTL-Ubi: A novel deep transfer learning method for effectively predicting ubiquitination sites of multiple species. Methods, 2021, 192, 103-111.
[http://dx.doi.org/10.1016/j.ymeth.2020.08.003] [PMID: 32791338]
[66]
Ao, C.; Zou, Q.; Yu, L. RFhy-m2G: Identification of RNA N2-methylguanosine modification sites based on random forest and hybrid features. Methods, 2022, 203, 32-39.
[http://dx.doi.org/10.1016/j.ymeth.2021.05.016] [PMID: 34033879]
[67]
Yang, H.; Luo, Y.; Ren, X.; Wu, M.; He, X.; Peng, B.; Deng, K.; Yan, D.; Tang, H.; Lin, H. Risk prediction of diabetes: Big data mining with fusion of multifarious physical examination indicators. Inf. Fusion, 2021, 75, 140-149.
[http://dx.doi.org/10.1016/j.inffus.2021.02.015]
[68]
Goceri, E. An application for automated diagnosis of facial dermatological diseases. İzmir Katip Çelebi Univ. Health Science. J., 2021, 6, 91-99.
[69]
Goceri, E. Medical image data augmentation: Techniques, comparisons and interpretations. Artif. Intell. Rev., 2023, 56, 12561-12605.
[70]
Goceri, E. Comparison of the impacts of dermoscopy image augmentation methods on skin cancer classification and a new augmentation method with wavelet packets. Int. J. Imaging Syst. Technol., 2023, 33(5), 1727-1744.
[http://dx.doi.org/10.1002/ima.22890]
[71]
Goceri, E. Image augmentation for deep learning based lesion classification from skin images. 2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS), 09-11 December 2020Genova, Italy 2020.
[72]
Goceri, E.J.P.I.C.C.G. Visualization, computer vision; Image processing. 6th International Conference On Big Data Analytics, D. M.; Intel, C., Analysis Of Capsule Networks For Image Classification, 2021, 53-60.
[73]
Goceri, E.J.P.I.C.C.G. Visualization, Computer Vision; Image Processing , t. t. I. C. o. C. S. C. 6th International Conference On Big Data Analytics, D. M.; Intel, C., Capsule Neural Networks In Classification Of Skin Lesions 2021.
[74]
Goceri, E. Classification of skin cancer using adjustable and fully convolutional capsule layers. Biomed. Signal Process. Control, 2023, 85, 104949.
[http://dx.doi.org/10.1016/j.bspc.2023.104949]

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