ArticleBMC genomics2018
Prediction of enhancer-promoter interactions via natural language processing.
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- Deep learning-drivenSynthetic and systems biotechnology · 2027Article
- MULTIMODAL CELL CONTEXT INSTRUCTION TUNING FOR CONDITIONAL DNA REGULATORY SEQUENCE GENERATION WITH LARGE LANGUAGE MODELS.Proceedings. International Conference on Image Processing · 2025Article
- Deep learning guided programmable design of Escherichia coli core promoters from sequence architecture to strength control.Nucleic acids research · 2025Article
- Machine learning for the prediction of augmented renal clearance (ARC) in patients with sepsis in critical care units.Scientific reports · 2025Article
- Class-balanced negative training sets for improving classifier model predictions of enhancer-promoter interactions.BMC bioinformatics · 2025Article
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- MLA-ac4C: A N4-Acetylcytidine Site Prediction Model Based on Multi-Layer Attention, Bidirectional GRU, and DenseNet.Current genomics · 2025Article
- A review on the applications of Transformer-based language models for nucleotide sequence analysis.Computational and structural biotechnology journal · 2025Review
- GATv2EPI: Predicting Enhancer-Promoter Interactions with a Dynamic Graph Attention Network.Genes · 2024Article
- Cheminformatic Identification of Tyrosyl-DNA Phosphodiesterase 1 (Tdp1) Inhibitors: A Comparative Study of SMILES-Based Supervised Machine Learning Models.Journal of personalized medicine · 2024Article
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- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- Computational methods for analysing multiscale 3D genome organization.Nature reviews. Genetics · 2024Review
- Puzzle Hi-C: An accurate scaffolding software.PloS one · 2024Article
- Computational methods for identifying enhancer-promoter interactions.Quantitative biology (Beijing, China) · 2023Review
- A survey on algorithms to characterize transcription factor binding sites.Briefings in bioinformatics · 2023Review
- A neural network model to screen feature genes for pancreatic cancer.BMC bioinformatics · 2023Article
- STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data.Bioinformatics (Oxford, England) · 2023Article
- HiChIPdb: a comprehensive database of HiChIP regulatory interactions.Nucleic acids research · 2023Article
- Applications of transformer-based language models in bioinformatics: a survey.Bioinformatics advances · 2023Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrecise identification of three-dimensional genome organization, especially enhancer-promoter interactions (EPIs), is important to deciphering gene regulation, cell differentiation and disease mechanisms. Currently, it is a challenging task to distinguish true interactions from other nearby non-interacting ones since the power of traditional experimental methods is limited due to low resolution or low throughput.
resultsWe propose a novel computational framework EP2vec to assay three-dimensional genomic interactions. We first extract sequence embedding features, defined as fixed-length vector representations learned from variable-length sequences using an unsupervised deep learning method in natural language processing. Then, we train a classifier to predict EPIs using the learned representations in supervised way. Experimental results demonstrate that EP2vec obtains F1 scores ranging from 0.841~ 0.933 on different datasets, which outperforms existing methods. We prove the robustness of sequence embedding features by carrying out sensitivity analysis. Besides, we identify motifs that represent cell line-specific information through analysis of the learned sequence embedding features by adopting attention mechanism. Last, we show that even superior performance with F1 scores 0.889~ 0.940 can be achieved by combining sequence embedding features and experimental features.
conclusionsEP2vec sheds light on feature extraction for DNA sequences of arbitrary lengths and provides a powerful approach for EPIs identification.
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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.