ArticleNature computational science2023
Predictive analyses of regulatory sequences with EUGENe.
Article in Nature computational science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
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Who cites it
14 citing papers in PubMed.
- Tangermeme: a toolkit for understanding cis-regulatory logic using deep learning models.Nature methods · 2026Article
- DeepGeSeq: deep learning library for genomic sequence modeling and analysis.Bioinformatics (Oxford, England) · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species.Nature methods · 2026Article
- gReLU: a comprehensive framework for DNA sequence modeling and design.Nature methods · 2025Article
- Genetic Deconvolution of Embryonic and Maternal Cell-Free DNA in Spent Culture Medium of Human Preimplantation Embryo Through Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- High-throughput quantitative assessment of ABA-responsive elements at single-nucleotide resolution.Quantitative biology (Beijing, China) · 2025Article
- Enhancers in Plant Development, Adaptation and Evolution.Plant & cell physiology · 2025Review
- Analysis-ready VCF at Biobank scale using Zarr.bioRxiv : the preprint server for biology · 2025Article
- Analysis-ready VCF at Biobank scale using Zarr.GigaScience · 2025Article
- AutoXAI4Omics: an automated explainable AI tool for omics and tabular data.Briefings in bioinformatics · 2024Article
- Decoding biology with massively parallel reporter assays and machine learning.Genes & development · 2024Review
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Deep learning has become a popular tool to study cis-regulatory function. Yet efforts to design software for deep-learning analyses in regulatory genomics that are findable, accessible, interoperable and reusable (FAIR) have fallen short of fully meeting these criteria. Here we present elucidating the utility of genomic elements with neural nets (EUGENe), a FAIR toolkit for the analysis of genomic sequences with deep learning. EUGENe consists of a set of modules and subpackages for executing the key functionality of a genomics deep learning workflow: (1) extracting, transforming and loading sequence data from many common file formats; (2) instantiating, initializing and training diverse model architectures; and (3) evaluating and interpreting model behavior. We designed EUGENe as a simple, flexible and extensible interface for streamlining and customizing end-to-end deep-learning sequence analyses, and illustrate these principles through application of the toolkit to three predictive modeling tasks. We hope that EUGENe represents a springboard towards a collaborative ecosystem for deep-learning applications in genomics research.
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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.