TERL: classification of transposable elements by convolutional neural networks.Transposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes.  Here we propose an approach, called transposable elements pepresentation learner (TERL), that preprocesses and transforms one-dimensional sequences into two-dimensional space data (i.e., image-like data of the sequences) and apply it to deep convolutional neural networks.  We have conducted six experiments to test the performance of TERL against other methods.  Therefore, TERL can learn how to predict any hierarchical level of the TEs classification system and is about 20 times and three orders of magnitude faster than TEclass and PASTEC, respectively https://github.com/muriloHoracio/TERL. 
TERL
TERL: classification of transposable elements by convolutional neural networks.Transposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes.  Here we propose an approach, called transposable elements pepresentation learner (TERL), that preprocesses and transforms one-dimensional sequences into two-dimensional space data (i.e., image-like data of the sequences) and apply it to deep convolutional neural networks.  We have conducted six experiments to test the performance of TERL against other methods.  Therefore, TERL can learn how to predict any hierarchical level of the TEs classification system and is about 20 times and three orders of magnitude faster than TEclass and PASTEC, respectively https://github.com/muriloHoracio/TERL. 
TERL
TERL: classification of transposable elements by convolutional neural networks.Transposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes.  Here we propose an approach, called transposable elements pepresentation learner (TERL), that preprocesses and transforms one-dimensional sequences into two-dimensional space data (i.e., image-like data of the sequences) and apply it to deep convolutional neural networks.  We have conducted six experiments to test the performance of TERL against other methods.  Therefore, TERL can learn how to predict any hierarchical level of the TEs classification system and is about 20 times and three orders of magnitude faster than TEclass and PASTEC, respectively https://github.com/muriloHoracio/TERL. 
TERL
TERL: classification of transposable elements by convolutional neural networks.Transposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes.  Here we propose an approach, called transposable elements pepresentation learner (TERL), that preprocesses and transforms one-dimensional sequences into two-dimensional space data (i.e., image-like data of the sequences) and apply it to deep convolutional neural networks.  We have conducted six experiments to test the performance of TERL against other methods.  Therefore, TERL can learn how to predict any hierarchical level of the TEs classification system and is about 20 times and three orders of magnitude faster than TEclass and PASTEC, respectively https://github.com/muriloHoracio/TERL. 
TERL
TERL: classification of transposable elements by convolutional neural networks.Transposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes.  Here we propose an approach, called transposable elements pepresentation learner (TERL), that preprocesses and transforms one-dimensional sequences into two-dimensional space data (i.e., image-like data of the sequences) and apply it to deep convolutional neural networks.  We have conducted six experiments to test the performance of TERL against other methods.  Therefore, TERL can learn how to predict any hierarchical level of the TEs classification system and is about 20 times and three orders of magnitude faster than TEclass and PASTEC, respectively https://github.com/muriloHoracio/TERL. 
TERL