DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes.

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 bidirectional long-short-term memory (BiLSTM). DeepReg reached a precision of 0.99, a recall of 0.97, and an F1-score of 0.98. The quality of our predictions, the bias-variance trade-off approach, and the characterization of new TF predictions were evaluated and compared against those prod

SUBMITTER: Ledesma-Dominguez L 

PROVIDER: S-EPMC11551149 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature