Abstract
The next-generation sequencing technologies decreased the cost of protein sequence identification. However, the cost of determining protein functions is still high, due to the laboratory methods needed. With that, computational models have been used to annotate protein functions. In this work, we present and discuss a new approach for protein Molecular Function prediction based on Transformers embeddings. Our method surpassed state-of-the-art classifiers when it used only the amino acid sequence as input and when it employed amino acid sequence and homology search in \(F_{\max }\) and AuPRC metrics.
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Acknowledgements
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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de Oliveira, G.B., Pedrini, H., Dias, Z. (2022). Protein Molecular Function Annotation Based on Transformer Embeddings. In: Xavier-Junior, J.C., Rios, R.A. (eds) Intelligent Systems. BRACIS 2022. Lecture Notes in Computer Science(), vol 13654 . Springer, Cham. https://doi.org/10.1007/978-3-031-21689-3_16
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