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English Information

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Authors
# Name
1 André Gambogi(andregambogi@dcc.ufmg.br)
2 Arthur Buzelin(arthurbuzelin@dcc.ufmg.br)
3 Gabriela Miserani(gabrielamiserani@dcc.ufmg.br)
4 Guilherme Evangelista(guilherme.evangelista@dcc.ufmg.br)
5 Pedro Bento(pedro.bento@dcc.ufmg.br)
6 Yan Aquino(yanaquino@dcc.ufmg.br)
7 Samira Malquias(samiramalaquias@dcc.ufmg.br)
8 Pedro Bacelar(pedro.bacelar)
9 Pedro Robles(pedroroblesduten@ufmg.br)
10 Wagner Meira Jr.(meira@dcc.ufmg.br)
11 Gisele Pappa(glpappa@dcc.ufmg.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Bento, P., Aquino, Y., Buzelin, A., Rigueira, P. B., Gambogi, A., Porf ́ırio, L. G., Doria, I., Anunciac ̧ ̃ao, S., Mendes, G., Minardi, R., Paim, A. A., Pappa, G. L., da Fonseca, F., and Meira Jr., W. (2025). A machine learning-guided approach for a multiepitope hiv vaccine design. Revista Eletrˆonica de Iniciac ̧ ̃ao Cient ́ıfica em Computac ̧ ̃ao, 23(1):118–123.
2 Buzelin, A., Dutenhefner, P. R., Rezende, T., Porfirio, L. G., Bento, P., Aquino, Y., Fernandes, J., Santana, C., Miana, G., Pappa, G. L., Ribeiro, A., and Jr, W. M. (2025). A cnn-based local-global self-attention via averaged window embeddings for hierarchical ecg analysis.
3 ESM Team (2024). Esm cambrian: Revealing the mysteries of proteins with unsupervised learning.Gupta, S., Kapoor, P., Chaudhary, K., Gautam, A., and Kumar, R. G. P. S. (2013). Toxinpred: a web server for the prediction of toxic peptides and proteins. Nucleic Acids Research, 41(W1):W196–W203.
4 Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., ˇZ ́ıdek, 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., and Hassabis, D. (2021). Highly accurate protein structure prediction with alphafold. Nature, 596(7873):583–589.
5 Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., dos Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., and Rives, A. (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 379(6637):1123–1130.
6 Morozov, V., Rodrigues, C. H. M., and Ascher, D. B. (2023). Csm-toxin: A web-server for predicting protein toxicity. Pharmaceutics, 15(2):431.
7 Naamati, G., Winter, E., and Linial, M. (2009). Clantox: a classifier of animal toxins.Nucleic Acids Research, 37(Web Server issue):W602–W607.
8 Pan, X., Zuallaert, J., Wang, X., Shen, H.-B., Campos, E. P., Marushchak, D. O., and Neve, W. D. (2020). Toxdl: deep learning using primary structure and domain embeddings for assessing protein toxicity. Bioinformatics, 36(21):5159–5168.
9 Rappuoli, R., Mandl, C. W., Black, S., and Gregorio, E. D. (2011). Vaccines for the twenty-first century society. Nature Reviews Immunology, 11(12):865–872.
10 Saha, S. and Raghava, G. P. (2007a). Btxpred: Support vector machine-based method for predicting bacterial toxins. BMC Bioinformatics, 8:463.
11 Sharma, N., Devi, N. L., Jain, S., and Raghava, G. P. (2022). Toxinpred2: an improved method for predicting toxicity of proteins. Briefings in Bioinformatics, 23(5):bbac174.
12 Sharma, N. and Raghava, G. P. (2024). Toxinpred 3.0: A deep learning-based model for peptide and protein toxicity prediction. Manuscript accessed via Elsevier; exact citation pending journal confirmation.
13 Zhu, L., Fang, Y., Liu, S., Shen, H.-B., Neve, W. D., and Pan, X. (2025). Toxdl 2.0: Protein toxicity prediction using a pretrained language model and graph neural networks.Computational and Structural Biotechnology Journal, 27:1538–1549.