Semi-supervised Predictive Clustering Trees for Multi-label Protein Subcellular Localization

Autor(es)

Alcantara, Leonardo U. ; Triguero, Isaac ; Cerri, Ricardo

• Tipo de documento: Artigo Científico
• Fonte: INTELLIGENT SYSTEMS, BRACIS 2024, PT II; v. 15413, p. 16-pg., 2025-01-01.

Resumo

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)

• Processo FAPESP: 16/25220-1 - Aprendizado de máquina multirrótulo para localização subcelular de proteínas
• Beneficiário: Leonardo Utida Alcântara
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica
• Processo FAPESP: 22/02981-8 - Detecção de novidade em fluxos contínuos de dados multirrótulo
• Beneficiário: Ricardo Cerri
• Modalidade de apoio: Auxílio à Pesquisa - Projeto Inicial
• Processo FAPESP: 17/24807-1 - Aprendizado ativo para localização subcelular de proteínas
• Beneficiário: Leonardo Utida Alcântara
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Iniciação Científica