ArticleBriefings in bioinformatics2021
Locating transcription factor binding sites by fully convolutional neural network.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Immune-evasive stem cells: engineering tolerance and reprogramming microenvironments for regenerative therapy.Stem cell research & therapy · 2026Review
- A Deep Learning Framework with Multi-perspective Feature Fusion for Transcription Factor Binding Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- OptimDase: An Algorithm for Predicting DNA Binding Sites with Combined Feature Encoding.Interdisciplinary sciences, computational life sciences · 2025Article
- Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts.ArXiv · 2025Article
- WSHNN: A Weakly Supervised Hybrid Neural Network for the Identification of DNA-protein Binding Sites.Current computer-aided drug design · 2025Article
- MLSNet: a deep learning model for predicting transcription factor binding sites.Briefings in bioinformatics · 2024Article
- Cross-Species Prediction of Transcription Factor Binding by Adversarial Training of a Novel Nucleotide-Level Deep Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
- BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning.Briefings in bioinformatics · 2024Article
- BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning.Briefings in bioinformatics · 2024Article
- Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.Briefings in bioinformatics · 2024Article
- Computational prediction and characterization of cell-type-specific and shared binding sites.Bioinformatics (Oxford, England) · 2023Article
- Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network.Frontiers in genetics · 2023Article
- DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.PLoS computational biology · 2022Article
- A learning-based method to predict LncRNA-disease associations by combining CNN and ELM.BMC bioinformatics · 2022Article
- Base-resolution prediction of transcription factor binding signals by a deep learning framework.PLoS computational biology · 2022Article
- Expectile Neural Networks for Genetic Data Analysis of Complex Diseases.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcription factors (TFs) play an important role in regulating gene expression, thus identification of the regions bound by them has become a fundamental step for molecular and cellular biology. In recent years, an increasing number of deep learning (DL) based methods have been proposed for predicting TF binding sites (TFBSs) and achieved impressive prediction performance. However, these methods mainly focus on predicting the sequence specificity of TF-DNA binding, which is equivalent to a sequence-level binary classification task, and fail to identify motifs and TFBSs accurately. In this paper, we developed a fully convolutional network coupled with global average pooling (FCNA), which by contrast is equivalent to a nucleotide-level binary classification task, to roughly locate TFBSs and accurately identify motifs. Experimental results on human ChIP-seq datasets show that FCNA outperforms other competing methods significantly. Besides, we find that the regions located by FCNA can be used by motif discovery tools to further refine the prediction performance. Furthermore, we observe that FCNA can accurately identify TF-DNA binding motifs across different cell lines and infer indirect TF-DNA bindings.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.