ArticleBioinformatics advances2025
Predicting gene expression using millions of yeast promoters reveals
Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- High-throughput yeast engineering in biofoundries: towards autonomous and scalable synthetic biology.FEMS yeast research · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Motivation: Gene regulation involves complex interactions between transcription factors. While early attempts to predict gene expression were trained using naturally occurring promoters, gigantic parallel reporter assays have vastly expanded potential training data. Despite this, it is still unclear how to best use deep learning to study gene regulation. Here, we investigate the association between promoters and expression using Camformer, a residual convolutional neural network that ranked fourth in the Random Promoter DREAM Challenge 2022. We present the original model trained on 6.7 million sequences and investigate 270 alternative models to find determinants of model performance. Finally, we use explainable AI to uncover regulatory signals. Results: Camformer accurately decodes the association between promoters and gene expression ( Availability and implementation: Data and code are available at: https://github.com/Bornelov-lab/Camformer.
Identifiers
What OpenQuestion holds
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.