Top-down Strategies for Hierarchical Classification of Transposable Elements with Neural Networks

Author(s)

Nakano, Felipe Kenji ; Pinto, Walter Jose ; Pappa, Gisele Lobo ; Cerri, Ricardo ; IEEE

• Document type: Journal article
• Source: 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2017-01-01.

Abstract

Transposable Elements are DNA sequences that can move from one place to another inside the genome of a cell. They are important for genetic variability, and can modify the functionality of genes. The correct classification of these elements is crucial to understand their role in the evolution of species. In this paper, we investigate Transposable Elements classification as a Hierarchical Classification problem using Machine Learning. We present new hierarchical datasets suitable to be used by Machine Learning methods, and also new hierarchical top-down classification strategies using neural networks. We compared our strategies with existing ones in the literature, and evaluated them using measures specific for hierarchical problems. Experiments showed that our proposal achieved better or competitive results than those found by other methods in the literature. (AU)

• FAPESP's process: 15/14300-1 - Hierarchical classification of transposable elements using machine learning
• Grantee: Ricardo Cerri
• Support Opportunities: Regular Research Grants