Improving Hierarchical Classification of Transposable Elements using Deep Neural Networks

Author(s)

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

• Document type: Journal article
• Source: 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2018-01-01.

Abstract

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)

• FAPESP's process: 16/12489-2 - Deep learning for hierarchical classification of transposable elements
• Grantee: Felipe Kenji Nakano
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 15/14300-1 - Hierarchical classification of transposable elements using machine learning
• Grantee: Ricardo Cerri
• Support Opportunities: Regular Research Grants
• FAPESP's process: 17/19264-9 - Active learning in hierarchical classification of transposable elements
• Grantee: Felipe Kenji Nakano
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree