ArticleNucleic acids research2012
Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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60 citing papers in PubMed, 129 citations in OpenAlex.
- Genomic language model for predicting enhancers and their allele-specific activity in the human genome.Bioinformatics (Oxford, England) · 2026Article
- Gaining insights into epigenetic memories through artificial intelligence and omics science in plants.Journal of integrative plant biology · 2025Review
- 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
- DeepDualEnhancer: A Dual-Feature Input DNABert Based Deep Learning Method for Enhancer Recognition.International journal of molecular sciences · 2024Article
- A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding.iScience · 2024Article
- Article
- Integration of transcription regulation and functional genomic data reveals lncRNA SNHG6's role in hematopoietic differentiation and leukemia.Journal of biomedical science · 2024Article
- Predicting active enhancers with DNA methylation and histone modification.BMC bioinformatics · 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
- iEnhancer-DCSV: Predicting enhancers and their strength based on DenseNet and improved convolutional block attention module.Frontiers in genetics · 2023Article
- iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models.Bioinformatics advances · 2023Article
- An Efficient Lightweight Hybrid Model with Attention Mechanism for Enhancer Sequence Recognition.Biomolecules · 2022Article
- Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework.PLoS computational biology · 2022Article
- Accurate prediction of functional states of cis-regulatory modules reveals common epigenetic rules in humans and mice.BMC biology · 2022Article
- A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns.Scientific reports · 2022Article
- Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition.Biomolecules · 2022Article
- From shallow to deep: some lessons learned from application of machine learning for recognition of functional genomic elements in human genome.Human genomics · 2022Review
- Epigenetic Regulation of Endothelial Cell Lineages During Zebrafish Development-New Insights From Technical Advances.Frontiers in cell and developmental biology · 2022Review
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The chemical modification of histones at specific DNA regulatory elements is linked to the activation, inactivation and poising of genes. A number of tools exist to predict enhancers from chromatin modification maps, but their practical application is limited because they either (i) consider a smaller number of marks than those necessary to define the various enhancer classes or (ii) work with an excessive number of marks, which is experimentally unviable. We have developed a method for chromatin state detection using support vector machines in combination with genetic algorithm optimization, called ChromaGenSVM. ChromaGenSVM selects optimum combinations of specific histone epigenetic marks to predict enhancers. In an independent test, ChromaGenSVM recovered 88% of the experimentally supported enhancers in the pilot ENCODE region of interferon gamma-treated HeLa cells. Furthermore, ChromaGenSVM successfully combined the profiles of only five distinct methylation and acetylation marks from ChIP-seq libraries done in human CD4(+) T cells to predict ∼21,000 experimentally supported enhancers within 1.0 kb regions and with a precision of ∼90%, thereby improving previous predictions on the same dataset by 21%. The combined results indicate that ChromaGenSVM comfortably outperforms previously published methods and that enhancers are best predicted by specific combinations of histone methylation and acetylation marks.
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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.