| Full text | |
| Author(s): |
de Mello, Felipe Lopes
;
de Oliveira, Gabriel Bianchin
;
Pedrini, Helio
;
Dias, Zanoni
;
Rutkowski, L
;
Scherer, R
;
Korytkowski, M
;
Pedrycz, W
;
Tadeusiewicz, R
;
Zurada, JM
Total Authors: 10
|
| Document type: | Journal article |
| Source: | ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2022, PT II; v. 13589, p. 9-pg., 2023-01-01. |
| Abstract | |
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 Fmax 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. (AU) | |
| FAPESP's process: | 17/16246-0 - Sensitive media analysis through deep learning architectures |
| Grantee: | Sandra Eliza Fontes de Avila |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 19/20875-8 - Chest X-ray image classification using deep neural networks |
| Grantee: | Vinicius Teixeira de Melo |
| Support Opportunities: | Scholarships in Brazil - Master |
| FAPESP's process: | 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 15/11937-9 - Investigation of hard problems from the algorithmic and structural stand points |
| Grantee: | Flávio Keidi Miyazawa |
| Support Opportunities: | Research Projects - Thematic Grants |