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Prediction of Protein Molecular Functions Using Transformers

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Artificial Intelligence and Soft Computing (ICAISC 2022)


At the end of 2021, there were more than 200 million proteins in which their molecular functions were still unknown. As the empirical determination of these functions is slow and expensive, several research groups around the world have applied machine learning to perform the prediction of protein functions. In this work, we evaluate the use of Transformer architectures to classify protein molecular functions. Our classifier uses the embeddings resulting from two Transformer-based architectures as input to a Multi-Layer Perceptron classifier. This model got \(F_{\max }\) of 0.562 in our database and, when we applied this model to the same database used by DeepGOPlus, we reached the value of 0.617, surpassing the best result available in the literature.

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This research was supported by São Paulo Research Foundation (FAPESP) [grant numbers 2015/11937-9, 2017/12646-3, 2017/16246-0, 2017/12646-3 and 2019/20875-8], the National Council for Scientific and Technological Development (CNPq) [grant numbers 161015/2021-2, 304380/2018-0 and 309330/2018-1], and Coordination for the Improvement of Higher Education Personnel (CAPES).

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Correspondence to Gabriel Bianchin de Oliveira .

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de Mello, F.L., de Oliveira, G.B., Pedrini, H., Dias, Z. (2023). Prediction of Protein Molecular Functions Using Transformers. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13589. Springer, Cham.

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