Supervised enhancer prediction with epigenetic pattern recognition and targeted validation

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Review and Evaluate the Bioinformatics Analysis Strategies of ATAC-seq and CUT&Tag Data

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DEK–nucleosome structure shows DEK modulates H3K27me3 and stem cell fate

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Identification and prediction of developmental enhancers in sea urchin embryos

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From Genotype to Phenotype: How Enhancers Control Gene Expression and Cell Identity in Hematopoiesis

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Towards Highly-Efficient k-Nearest Neighbor Algorithm for Big Data Classification

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Recapitulation of patient-specific 3D chromatin conformation using machine learning

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TGFβ signaling promotes cell cycle progression and resistance to the CDK4/6 inhibitor palbociclib through SOX4 transcriptional modulation in breast cancer cells

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Analysis of the landscape of human enhancer sequences in biological databases

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Self-distillation improves self-supervised learning for DNA sequence inference

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Global properties of regulatory sequences are predicted by transcription factor recognition mechanisms

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Cis-regulatory atlas of primary human CD4+ T cells

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ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements

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AthEDL: Identifying Enhancers in Arabidopsis thaliana Using an Attention-based Deep Learning Method

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Resolving a Systematic Error in STARR-seq for Quantitative Enhancer Activity MappingPreprint

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A map of enhancer regions in primary human neural progenitor cells using capture STARR-seqPreprint

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Minimizing far-extending chromatin perturbation in genome editing preserves stem cell identity

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Integration of CRISPR‐Cas9 screens and multi‐omics profiling reveals chromodomain helicase DNA binding protein 7‐angiopoietin‐1 as a novel multidrug resistance axis in acute myeloid leukemia

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Analysis of Functional Promoter of Camel FGF21 Gene and Identification of Small Compounds Targeting FGF21 Protein

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The Impact of Data Normalization on KNN Rendering

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Identification and Validation of eRNA as a Prognostic Indicator for Cervical Cancer

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ChromActivity: integrative epigenomic and functional characterization assay based annotation of regulatory activity across diverse human cell types

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Xu, D., Forbes, A. N., Cohen, S. M., Palladino, A. C., Karadimitriou, T., & Khurana, E. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.16.468857 · Cited by 3 · see citing works

PorcineAI-Enhancer: Prediction of Pig Enhancer Sequences Using Convolutional Neural Networks

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Functional characteristics and computational model of abundant hyperactive loci in the human genomePreprint

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Simultaneous Prediction of Functional States and Types of cis -regulatory Modules Reveals Their Prevalent Dual Uses as Enhancers and SilencersPreprint

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Crosstalk between chromatin and Shavenbaby defines transcriptional output along the Drosophila intestinal stem cell lineage

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An Epigenomic fingerprint of human cancers by landscape interrogation of super enhancers at the constituent level

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Prediction of gene expression using histone modification patterns extracted by Particle Swarm Optimization

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Functional Validation of Noncoding Variants Associated With Nonsyndromic Orofacial Cleft

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Predictive design of tissue-specific mammalian enhancers that function in the mouse embryo

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A sparse and wide neural network model for DNA sequences

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Functional characteristics and computational model of abundant hyperactive loci in the human genome

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Induce Pluripotency via Specific distal enhancer-promoter associationsPreprint

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Validation of Enhancer Regions in Primary Human Neural Progenitor Cells using Capture STARR-seqPreprint

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Functional characteristics and computational model of abundant hyperactive loci in the human genomePreprint

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Inhibition of cytosolic DNA sensing and transposon activity safeguards pluripotencyPreprint

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Crosstalk between chromatin and the transcription factor Shavenbaby defines transcriptional output along the Drosophila intestinal stem cell lineagePreprint

Mancheno-Ferris, A., Immarigeon, C., Rivero, A., Depierre, D., Chanard, N., Fosseprez, O., Aughey, G., Lhoumaud, P., Anglade, J., Southall, T. D., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.03.522588

Prediction accuracy of regulatory elements from sequence varies by functional sequencing technique

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Identification of Cancer-specific Constituent Elements inside Super-enhancers (cSEAdb) v1Preprint

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Sequence characteristics and an accurate model of abundant hyperactive loci in the human genomePreprint

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Self-Distillation Improves DNA Sequence InferencePreprint

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A Type-2 Fuzzy Logic-Based Explainable Artificial Intelligence for the Prediction of Enhancers

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Functional interrogation of candidate cis -regulatory elements at the LDLR locusPreprint

Leix, K., Serrano-Zayas, C., Graham, S. E., & Emmer, B. T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.20.644385

scPPF: Predicting Promoter Fragments in scATAC-seq Based on Transcription Factor Motifs

Li, J., Li, H., Wang, F., Yuan, L., & Zhao, Y. (2025) · Current Bioinformatics · doi:10.2174/0115748936385361250710042542

Additional file 6 of Global properties of regulatory sequences are predicted by transcription factor recognition mechanisms

Patel, Z. M., & Hughes, T. R. (2021) · Figshare · doi:10.6084/m9.figshare.16767622

Additional file 3 of Identification and prediction of developmental enhancers in sea urchin embryos

Arenas‐Mena, C., Miljovska, S., Rice, E. J., Gurges, J., Shashikant, T., Wang, Z., Ercan, S., & Danko, C. G. (2021) · Figshare · doi:10.6084/m9.figshare.16835059

Additional file 1 of Identification and prediction of developmental enhancers in sea urchin embryos

Arenas‐Mena, C., Miljovska, S., Rice, E. J., Gurges, J., Shashikant, T., Wang, Z., Ercan, S., & Danko, C. G. (2021) · Figshare · doi:10.6084/m9.figshare.16835053

Additional file 4 of Cis-regulatory atlas of primary human CD4+ T cells

Stefan, K., & Barski, A. (2024) · Figshare · doi:10.6084/m9.figshare.26588769

Additional file 5 of Cis-regulatory atlas of primary human CD4+ T cells

Stefan, K., & Barski, A. (2024) · Figshare · doi:10.6084/m9.figshare.26588772

Additional file 5 of Identification and prediction of developmental enhancers in sea urchin embryos

Arenas‐Mena, C., Miljovska, S., Rice, E. J., Gurges, J., Shashikant, T., Wang, Z., Ercan, S., & Danko, C. G. (2021) · Figshare · doi:10.6084/m9.figshare.16835065

Additional file 6 of Identification and prediction of developmental enhancers in sea urchin embryos

Arenas‐Mena, C., Miljovska, S., Rice, E. J., Gurges, J., Shashikant, T., Wang, Z., Ercan, S., & Danko, C. G. (2021) · Figshare · doi:10.6084/m9.figshare.16835068

Additional file 2 of Global properties of regulatory sequences are predicted by transcription factor recognition mechanisms

Patel, Z. M., & Hughes, T. R. (2021) · Figshare · doi:10.6084/m9.figshare.16767610

EnhancerDetector: enhancer discovery from human to fly via interpretable deep learning

Solis, L. M., Sterling-Lentsch, G., Halfon, M. S., & Girgis, H. Z. (2026) · BMC Genomics · doi:10.1186/s12864-026-13262-0 · Cited by 2 · see citing works

Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns

Osmala, M., & Lähdesmäki, H. J. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03621-3 · Cited by 12 · see citing works

A comprehensive revisit of the machine‐learning tools developed for the identification of enhancers in the human genome

Phan, L. T., Oh, C., He, T., & Manavalan, B. (2023) · PROTEOMICS · doi:10.1002/pmic.202200409 · Cited by 11 · see citing works

Cross-species enhancer prediction using machine learning

MacPhillamy, C., Alinejad‐Rokny, H., Pitchford, W. S., & Low, W. Y. (2022) · Genomics · doi:10.1016/j.ygeno.2022.110454 · Cited by 19 · see citing works

Integrative machine learning framework for the identification of cell-specific enhancers from the human genome

Basith, S., Hasan, M. M., Lee, G., Wei, L., & Manavalan, B. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab252 · Cited by 68 · see citing works