Improving Hierarchical Classification of Transposable Elements using Deep Neural Networks

Autor(es)

Nakano, Felipe Kenji ; Mastelini, Saulo Martiello ; Barbon, Sylvio, Jr. ; Cerri, Ricardo ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2018-01-01.

Resumo

Transposable Elements (TEs) are DNA sequences capable of moving within a cell's genome. Their transposition has many effects in genomes, such as creating genetic variability and promoting changes in genes' functionality. Recently, TEs classification has been addressed using Machine Learning (ML), more specifically by Hierarchical Classification (HC) methods. Such works proved to be superior than previous ones in the literature. However, there is still room for improvement performance wise. In this direction, Deep Neural Networks (DNNs) have attracted a lot of attention in ML. In particular, Stacked Denoising AutoEncoders (DAEs) and Deep Multi Layer-Perceptrons (MLPs) are known to provide outstanding results. By performing an extensive evaluation, our results point out that DNNs can enhance the performance of HC methods, being able to push further the state-of-art in TEs' classification. (AU)

• Processo FAPESP: 16/12489-2 - Deep learning para classificação hierárquica de elementos Transponníveis
• Beneficiário: Felipe Kenji Nakano
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• Processo FAPESP: 15/14300-1 - Classificação hierárquica de elementos transponíveis utilizando aprendizado de máquina
• Beneficiário: Ricardo Cerri
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 17/19264-9 - Active learning para classificação hierárquica de elementos transponíveis
• Beneficiário: Felipe Kenji Nakano
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Mestrado