DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes
Leonardo Ledesma-Dominguez,
Erik Carbajal-Degante,
Gabriel Moreno-Hagelsieb
et al.
Abstract:Deep learning models (DLMs) have gained importance in predicting, detecting, translating, and classifying a diversity of inputs. In bioinformatics, DLMs have been used to predict protein structures, transcription factor-binding sites, and promoters. In this work, we propose a hybrid model to identify transcription factors (TFs) among prokaryotic and eukaryotic protein sequences, named Deep Regulation (DeepReg) model. Two architectures were used in the DL model: a convolutional neural network (CNN), and a bidir… Show more
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