ArticleScientific reports2024
DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Early Evolution of the Prokaryotic Transcription Factor Repertoire.Genome biology and evolution · 2026Article
- Programmed meiotic errors facilitate dichotomous sperm production in the silkworm,Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A systematic guide for identifying transcription factors that directly regulate the expression of a gene of interest.Genome research · 2026Review
- Synthetic tunable promoters for flexible control of multi-gene expression in mammalian cells.Journal of advanced research · 2025Article
- Current progress of digital twin construction using medical imaging.Journal of applied clinical medical physics · 2025Review
- Transcription factor prediction using protein 3D secondary structures.Bioinformatics (Oxford, England) · 2024Article
- Microbial biosensors for diagnostics, surveillance and epidemiology: Today's achievements and tomorrow's prospects.Microbial biotechnology · 2024Review
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
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 produced by DeepTFactor, as well as against experimental data from three model organisms. Predictions based on our DLM tended to exhibit less variance and bias than those from DeepTFactor, thus increasing reliability and decreasing overfitting.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.