References
- N. Buton, F. Coste, and Y. Le Cunff, “Predicting Enzymatic Function of Protein Sequences With Attention,” Bioinformatics, vol. 39, no. 10, Oct. 2023, doi: 10.1093/bioinformatics/btad620.
- Y. González Valle, D. Galpert, and R. Molina-Ruiz, “Integración De Rasgos Y Aprendizaje SemiSupervisado Para La Clasificación Funcional De Enzimas Utilizando KMeans De Spark,” Revista Cubana de Ciencias Informáticas, vol. 14, no. 4, 2020.
- Y. González Valle, D. Galpert, and R. MolinaRuiz, “Agrupamiento Funcional De Enzimas GH-70 Utilizando Aprendizaje Semi-Supervisado Y Apache Spark,” Revista Cubana de Transformación Digital, pp. 14–32, 2021.
- H. Chehili, S. E. Aliouane, A. Bendahmane, and M. A. Hamidechi, “DeepEnz: Prediction Of Enzyme Classification By Deep Learning,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 22, no. 2, 2021, doi: 10.11591/ije ecs.v22.i2.pp1108-1115.
- Z. Tao, B. Dong, Z. Teng, and Y. Zhao, “The Classification of Enzymes by Deep Learning,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.202 0.2992468.
- N. Ibtehaz and D. Kihara, “Application of Sequence Embedding in Protein Sequence-Based Predictions,” in Machine Learning in Bioinformatics of Protein Sequences: Algorithms, Databases and Resources for Modern Protein Bioinformatics, 2022. doi: 10.1142/9789811258589_0002.
- K. K. Yang, Z. Wu, C. N. Bedbrook, and F. H. Arnold, “Learned Protein Embeddings For Machine Learning,” Bioinformatics, vol. 34, no. 15, pp. 2642–2648, Aug. 2018, doi: 10.1093/bi oinformatics/bty178.
- C. Marquet et al., “Embeddings From Protein Language Models Predict Conservation And Variant Effects,” Hum Genet, vol. 141, no. 10, 2022, doi: 10.1007/s00439-021-02411-y.
- M. M. Moya and D. R. Hush, “Network Constraints And Multi-Objective Optimization For One-Class Classification,” Neural Networks, vol. 9, no. 3, 1996, doi: 10.1016/0893-6080(95)00120-4.
- M. Sakurada and T. Yairi, “Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction,” in ACM International Conference Proceeding Series, 2014. doi: 10.1145/2689746.2689747.
- K. Pawar and V. Attar, “Deep Learning Model Based on Cascaded Autoencoders and One-Class Learning For Detection And Localization Of Anomalies From Surveillance Videos,” IET Biom, vol. 11, no. 4, 2022, doi: 10.1049/bme2.12064.
- L. López, N. Acosta-Mendoza, and A. Gago-Alonso, “Detección De Anomalías Basada En Aprendizaje Profundo,” Revista de Ciencias Informáticas, vol. 13, no. 3, 2020.
- M. V. Nallapareddy and R. Dwivedula, “ABLE: Attention Based Learning For Enzyme Classification,” Comput Biol Chem, vol. 94, p. 107558, 2021, doi: https://doi.org/10.1016/j.compbiolchem.2021.107558.
- R. Atienza, Advanced Deep Learning with Keras. 2018.
- L. Wang, H. Zhang, W. Xu, Z. Xue, and Y. Wang, “Deciphering The Protein Landscape With Protflash, A Lightweight Language Model,” Cell Rep PhysSci, vol. 4, no. 10, p. 101600, 2023, doi: https://doi.org/10.1016/j.xcrp.2023.101600.
- K. Cabello-Solorzano, I. Ortigosa de Araujo, M. Peña, L. Correia, and A. J. Tallón-Ballesteros, “The Impact of Data Normalization on the Accuracy of Machine Learning Algorithms: A Comparative Analysis,” 2023. doi: 10.1007/978-3-031-4253 6-3_33.
- N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-Sampling Technique,” Journal of Artificial Intelligence Research, vol. 16, 2002, doi: 10.1613/jair.953.
- G. Douzas, F. Bacao, and F. Last, “Improving Imbalanced Learning Through A Heuristic Oversampling Method Based On K-Means And SMOTE,” Inf Sci (N Y), vol. 465, 2018, doi: 10.1 016/j.ins.2018.06.056.
- H. Han, W. Y. Wang, and B. H. Mao, “Borderline-SMOTE: A New Over-Sampling Method In Imbalanced Data Sets Learning,” in Lecture Notes in Computer Science, 2005. doi: 10.1007/115380 59_91.
- Aurélien Géaron, Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow: Concepts, Tools, And Techniques to Build Intelligent Systems. 2022.
- D. P. Kingma and J. L. Ba, “Adam: A Method For Stochastic Optimization,” in 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 2015.
- R. Dhanuka, A. Tripathi, and J. P. Singh, “A Semi-Supervised Autoencoder-Based Approach for Protein Function Prediction,” IEEE J Biomed Health Inform, vol. 26, no. 10, pp. 4957–4965, Oct. 2022, doi: 10.1109/JBHI.2022.3163150.
- N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way To Prevent Neural Networks From Overfitting,” Journal of Machine Learning Research, vol. 15, 2014.
- T. Dozat, “Incorporating Nesterov Momentum into Adam,” ICLR Workshop, no. 1, 2016.
- H. Bin Shen and K. C. Chou, “EzyPred: A Top– Down Approach For Predicting Enzyme Functional Classes And Subclasses,” Biochem Biophys Res Commun, vol. 364, no. 1, pp. 53–59, Dec. 2007, doi: 10.1016/J.BBRC.2007.09.098.
- A. Dalkiran, A. S. Rifaioglu, M. J. Martin, R. Cetin-Atalay, V. Atalay, and T. Doğan, “ECPred: A Tool For The Prediction Of The Enzymatic Functions Of Protein Sequences Based On The EC Nomenclature,” BMC Bioinformatics, vol. 19, no. 1, Sep. 2018, doi: 10.1186/s12859-018-2368-y.
- T. Sanderson, M. L. Bileschi, D. Belanger, and L. J. Colwell, “ProteInfer, Deep Neural Networks for Protein Functional Inference,” Elife, vol. 12, 2023, doi: 10.7554/eLife.80942.