| Full text | |
| Author(s): |
Alcantara, Leonardo U.
;
Triguero, Isaac
;
Cerri, Ricardo
Total Authors: 3
|
| Document type: | Journal article |
| Source: | INTELLIGENT SYSTEMS, BRACIS 2024, PT II; v. 15413, p. 16-pg., 2025-01-01. |
| Abstract | |
Protein subcellular localization is an important classification task because the location of proteins in a cell is directly linked to their functions. Since a protein can act at two or more locations simultaneously, multi-label classification algorithms are necessary. The currently used algorithms are usually based on supervised learning, which presents some disadvantages such as (i) a need for a large amount of labeled instances for training; (ii) a waste of valuable information that labeled instances can provide; and (iii) a high cost involved in obtaining labeled instances for training. To overcome these disadvantages, semi-supervised learning can be applied, where classifiers exploit both labeled and unlabeled data. Thus, in this paper, we propose a new semi-supervised algorithm for multi-label protein subcellular localization. Our proposal is based on decision tree classifiers induced using predictive clustering trees. We investigate many semi-supervised protein subcellular localization scenarios to test whether unlabeled instances can improve the multi-label classification process. Our results show that the proposal can achieve competitive or better results when compared to the pure supervised version of the predictive clustering trees. (AU) | |
| FAPESP's process: | 16/25220-1 - Multi-label machine learning for protein subcellular localization |
| Grantee: | Leonardo Utida Alcântara |
| Support Opportunities: | Scholarships in Brazil - Scientific Initiation |
| FAPESP's process: | 22/02981-8 - Novelty detection in multi-label data streams classification |
| Grantee: | Ricardo Cerri |
| Support Opportunities: | Research Grants - Initial Project |
| FAPESP's process: | 17/24807-1 - Active learning for protein subcellular localization |
| Grantee: | Leonardo Utida Alcântara |
| Support Opportunities: | Scholarships abroad - Research Internship - Scientific Initiation |