ArticleBriefings in bioinformatics2024
Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 17 citations in OpenAlex.
- TSProm: deep learning framework to predict tissue-specific regulatory logic.NAR genomics and bioinformatics · 2026Article
- SEdb 3.0: a comprehensive super-enhancer database across multiple species.Nucleic acids research · 2026Article
- GRANet: a graph residual attention network for gene regulatory network inference.Briefings in bioinformatics · 2025Article
- A novel deep learning framework with dynamic tokenization for identifying chromatin interactions along with motif importance investigation.Briefings in bioinformatics · 2025Article
- DeepPD: A Deep Learning Method for Predicting Peptide Detectability Based on Multi-feature Representation and Information Bottleneck.Interdisciplinary sciences, computational life sciences · 2025Article
- F-box proteins at the crossroads of ubiquitination and tumor immunity: regulatory networks and immunotherapy strategies.Frontiers in immunology · 2025Review
- AttnW2V-Enhancer: Leveraging attention and Word2Vec for enhanced enhancer prediction.Computational and structural biotechnology journal · 2025Article
- Utilizing a deep learning model based on BERT for identifying enhancers and their strength.PloS one · 2025Article
- MFPSP: Identification of fungal species-specific phosphorylation site using offspring competition-based genetic algorithm.PLoS computational biology · 2024Article
- IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification.PLoS computational biology · 2024Article
- Machine Learning-Based Integration of Single-Cell and Bulk Transcriptome Reveals Coagulation Signature and Phenotypic Heterogeneity in Hepatocellular Carcinoma.IET systems biologyArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Enhancers, noncoding DNA fragments, play a pivotal role in gene regulation, facilitating gene transcription. Identifying enhancers is crucial for understanding genomic regulatory mechanisms, pinpointing key elements and investigating networks governing gene expression and disease-related mechanisms. Existing enhancer identification methods exhibit limitations, prompting the development of our novel multi-input deep learning framework, termed Enhancer-MDLF. Experimental results illustrate that Enhancer-MDLF outperforms the previous method, Enhancer-IF, across eight distinct human cell lines and exhibits superior performance on generic enhancer datasets and enhancer-promoter datasets, affirming the robustness of Enhancer-MDLF. Additionally, we introduce transfer learning to provide an effective and potential solution to address the prediction challenges posed by enhancer specificity. Furthermore, we utilize model interpretation to identify transcription factor binding site motifs that may be associated with enhancer regions, with important implications for facilitating the study of enhancer regulatory mechanisms. The source code is openly accessible at https://github.com/HaoWuLab-Bioinformatics/Enhancer-MDLF.
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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 Socratic graph.