ArticleCell systems2025
Active learning of enhancers and silencers in the developing neural retina.
Article in Cell systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Synthetic Regulatory Genomics.Annual review of genomics and human genetics · 2026Review
- Iterative design of a NAND hybrid riboswitch by deep batch Bayesian optimization.Nucleic acids research · 2026Article
- Molecular circuits for genomic recording of cellular events.Trends in genetics : TIG · 2025Review
- Iterative deep learning design of human enhancers exploits condensed sequence grammar to achieve cell-type specificity.Cell systems · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Optimization of regulatory DNA with active learning.Computational and structural biotechnology journal · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Deep learning is a promising strategy for modeling cis-regulatory elements. However, models trained on genomic sequences often fail to explain why the same transcription factor can activate or repress transcription in different contexts. To address this limitation, we developed an active learning approach to train models that distinguish between enhancers and silencers composed of binding sites for the photoreceptor transcription factor cone-rod homeobox (CRX). After training the model on nearly all bound CRX sites from the genome, we coupled synthetic biology with uncertainty sampling to generate additional rounds of informative training data. This allowed us to iteratively train models on data from multiple rounds of massively parallel reporter assays. The ability of the resulting models to discriminate between CRX sites with identical sequence but opposite functions establishes active learning as an effective strategy to train models of regulatory DNA. A record of this paper's transparent peer review process is included in the supplemental information.
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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 OpenQuestion graph.