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Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Editorial

Explainable Artificial Intelligence for Protein Function Prediction: A Perspective View

Author(s): Nguyen Quoc Khanh Le*

Volume 18, Issue 3, 2023

Published on: 09 March, 2023

Page: [205 - 207] Pages: 3

DOI: 10.2174/1574893618666230220120449

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[1]
Wan C, Jones DT. Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks. Nat Mach Intell 2020; 2(9): 540-50.
[http://dx.doi.org/10.1038/s42256-020-0222-1]
[2]
Zou Y, Wu H, Guo X, et al. MK-FSVM-SVDD: A multiple kernel-based fuzzy SVM model for predicting DNA-binding proteins via support vector data description. Curr Bioinform 2021; 16(2): 274-83.
[http://dx.doi.org/10.2174/2212392XMTA3bMTYiy]
[3]
Wang XF, Lu F, Du ZY, Li QM. Prediction model of thermophilic protein based on stacking method. Curr Bioinform 2021; 16(10): 1328-40.
[http://dx.doi.org/10.2174/1574893616666210727152018]
[4]
Le NQK, Ou YY. Incorporating efficient radial basis function networks and significant amino acid pairs for predicting GTP binding sites in transport proteins. BMC Bioinformatics 2016; 17(S19): 501.
[http://dx.doi.org/10.1186/s12859-016-1369-y] [PMID: 28155651]
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Zou S, Wang L, Lu H, Shang C, Cheng L, Yang S. A novel method for predicting essential proteins by integrating multidimensional biological attribute information and topological properties. Curr Bioinform 2022; 17(4): 369-79.
[http://dx.doi.org/10.2174/1574893617666220304201507]
[6]
Le NQK, Nguyen BP. Prediction of FMN binding sites in electron transport chains based on 2-D CNN and PSSM profiles. IEEE/ACM Trans Comput Biol Bioinformatics 2021; 18(6): 2189-97.
[http://dx.doi.org/10.1109/TCBB.2019.2932416] [PMID: 31380767]
[7]
Khanh Le NQ, Nguyen QH, Chen X, Rahardja S, Nguyen BP. Classification of adaptor proteins using recurrent neural networks and PSSM profiles. BMC Genomics 2019; 20(S9): 966.
[http://dx.doi.org/10.1186/s12864-019-6335-4] [PMID: 31874633]
[8]
Wang Y, Luo X, Zou Q. Effector-GAN: Prediction of fungal effector proteins based on pretrained deep representation learning methods and generative adversarial networks. Bioinformatics 2022; 38(14): 3541-8.
[http://dx.doi.org/10.1093/bioinformatics/btac374] [PMID: 35640972]
[9]
Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021; 596(7873): 583-9.
[http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]
[10]
Do DT, Le NQK. A sequence-based approach for identifying recombination spots in Saccharomyces cerevisiae by using hyper-parameter optimization in FastText and support vector machine. Chemom Intell Lab Syst 2019; 194: 103855.
[http://dx.doi.org/10.1016/j.chemolab.2019.103855]
[11]
Le NQK, Ho QT. Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes. Methods 2022; 204: 199-206.
[http://dx.doi.org/10.1016/j.ymeth.2021.12.004] [PMID: 34915158]
[12]
Wolfe JC, Mikheeva LA, Hagras H, Zabet NR. An explainable artificial intelligence approach for decoding the enhancer histone modifications code and identification of novel enhancers in Drosophila. Genome Biol 2021; 22(1): 308.
[http://dx.doi.org/10.1186/s13059-021-02532-7] [PMID: 34749786]
[13]
Tharmakulasingam M, Gardner B, Ragione RL, Fernando A. Explainable deep learning approach for multilabel classification of antimicrobial resistance with missing labels. IEEE Access 2022; 10(113073): 85.
[http://dx.doi.org/10.1109/ACCESS.2022.3216896]
[14]
Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 2017; 30.
[15]
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[http://dx.doi.org/10.1145/2939672.2939778]

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