MachineLearning approaches to detect and classify Tranposable Elements

Transposable elements (TEs) are mobile genetic elements able to move and, occasionally, increase their copy numbers in genomes of virtually all organisms. Increasing reports indicate that they are key elements involved in crucial genomic functions. They are also one of the main contributors to genetic diversity and rapid genome size variation. Despite their essential impact on genomes, the detection and classification of TEs in genome sequences via bioinformatics analyses remain tedious tasks. Machine learning (ML) approaches were recently evaluated for TE datasets, demonstrating promising results.