ArticlePLoS computational biology2013
RFECS: a random-forest based algorithm for enhancer identification from chromatin state.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers.
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129 citing papers in PubMed, 244 citations in OpenAlex.
- Genomic language model for predicting enhancers and their allele-specific activity in the human genome.Bioinformatics (Oxford, England) · 2026Article
- Deep learning and attention mechanisms to identify key genes and their implications for the origin of insect wings.Scientific reports · 2026Article
- CBLN2 promoter enables genetic access to wide-field neurons of the tree shrew superior colliculus.Cell reports methods · 2026Article
- EnhancerMatcher: comparing cell-type-specific enhancer activity of DNA sequences using deep convolutional neural networks and explainable AI.NAR genomics and bioinformatics · 2025Article
- Article
- Enhancer reprogramming: critical roles in cancer and promising therapeutic strategies.Cell death discovery · 2025Review
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- A BERT-based rice enhancer identification model combined with sequence-representation differential entropy interpretation.Frontiers in plant science · 2025Article
- AttnW2V-Enhancer: Leveraging attention and Word2Vec for enhanced enhancer prediction.Computational and structural biotechnology journal · 2025Article
- Ascites-derived hsa-miR-181a-5p serves as a prognostic marker for gastric cancer-associated malignant ascites.BMC genomics · 2024Article
- A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding.iScience · 2024Article
- Article
- ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction.Briefings in bioinformatics · 2024Article
- DeepRegFinder: deep learning-based regulatory elements finder.Bioinformatics advances · 2024Article
- Predicting active enhancers with DNA methylation and histone modification.BMC bioinformatics · 2023Article
- PorcineAI-Enhancer: Prediction of Pig Enhancer Sequences Using Convolutional Neural Networks.Animals : an open access journal from MDPI · 2023Article
- iEnhancer-DCSA: identifying enhancers via dual-scale convolution and spatial attention.BMC genomics · 2023Article
- Computational methods for identifying enhancer-promoter interactions.Quantitative biology (Beijing, China) · 2023Review
- A Novel Repetition Frequency-Based DNA Encoding Scheme to Predict Human and Mouse DNA Enhancers with Deep Learning.Biomimetics (Basel, Switzerland) · 2023Article
- Decoding enhancer complexity with machine learning and high-throughput discovery.Genome biology · 2023Review
69 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
Transcriptional enhancers play critical roles in regulation of gene expression, but their identification in the eukaryotic genome has been challenging. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of cell types or chromatin marks have previously been investigated for this purpose, leaving the question unanswered whether there exists an optimal set of histone modifications for enhancer prediction in different cell types. Here, we address this issue by exploring genome-wide profiles of 24 histone modifications in two distinct human cell types, embryonic stem cells and lung fibroblasts. We developed a Random-Forest based algorithm, RFECS (Random Forest based Enhancer identification from Chromatin States) to integrate histone modification profiles for identification of enhancers, and used it to identify enhancers in a number of cell-types. We show that RFECS not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify the most informative and robust set of three chromatin marks for enhancer prediction.
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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.