Identifying Regulatory Elements via Deep Learning
importance 6/10
ai assisted
Genomics and Chromatin Dynamics regulatory genomics stage: research
Problem
Identifying gene regulatory elements such as protein-DNA binding sites from DNA/RNA sequence and epigenomic data.
Why it matters
Understanding gene regulation underpins diagnosis and treatment of genetic disease.
What the AI did
Reviews deep neural networks that analyze DNA/RNA sequences and epigenomic data to predict regulatory elements, including DeepBind and successors for protein-DNA binding.
Results
not stated in source
Impact
Evidence supports that deep learning methods have been successfully applied across regulatory genomics domains.
Record
| Venue | Annual Review of Biomedical Data Science |
|---|---|
| Published | 2020 |
| Work type | review |
| Authors | Mira Barshai, Eitamar Tripto, Yaron Orenstein |
| Citations | 23 (OpenAlex, at import time) |
| References | 102 |
| Model or method | DeepBind and successors (deep neural networks) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1146/annurev-biodatasci-022020-021940 |
| Open access | hybrid |
| Keywords | deep learning, regulatory genomics, protein-DNA binding, epigenomics, review |
Importance rating
6
significant — thousands to millions affected
Review of deep learning across regulatory genomics (protein-DNA/RNA binding, epigenomics); abstract ties gene regulation to genetic disease diagnosis and treatment, a foundational field.