ArticleNature machine intelligence2023
Hypergraph factorization for multi-tissue gene expression imputation.
Article in Nature machine intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
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
12 citing papers in PubMed.
- Estimating genotype-tissue specific gene expression using hybrid deep learning.Communications biology · 2026Article
- MultiCell: geometric learning in multicellular development.Nature methods · 2026Article
- OmiGA for ultra-efficient molecular quantitative trait loci mapping.Nature communications · 2026Article
- Tensor decomposition of multi-dimensional splicing events across multiple tissues to identify splicing-mediated risk genes associated with complex traits.PLoS computational biology · 2025Article
- The Farm Animal Genotype-Tissue Expression (FarmGTEx) Project.Nature genetics · 2025Review
- LungGENIE: the lung gene-expression and network imputation engine.BMC genomics · 2025Article
- Salivary cortisol in long COVID: a marker of broader stress system and circadian rhythm dysregulation.Frontiers in cellular and infection microbiology · 2025Article
- Higher order interaction analysis quantifies coordination in the epigenome revealing novel biological relationships in Kabuki syndrome.Briefings in bioinformatics · 2024Article
- Learning collective cell migratory dynamics from a static snapshot with graph neural networks.ArXiv · 2024Article
- Article
- SAF: Smart Aggregation Framework for Revealing Atoms Importance Rank and Improving Prediction Rates in Drug Discovery.Journal of chemical information and modeling · 2024Article
- Transcriptome-Wide Association Studies (TWAS): Methodologies, Applications, and Challenges.Current protocols · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Integrating gene expression across tissues and cell types is crucial for understanding the coordinated biological mechanisms that drive disease and characterise homeostasis. However, traditional multitissue integration methods cannot handle uncollected tissues or rely on genotype information, which is often unavailable and subject to privacy concerns. Here we present HYFA (Hypergraph Factorisation), a parameter-efficient graph representation learning approach for joint imputation of multi-tissue and cell-type gene expression. HYFA is genotype-agnostic, supports a variable number of collected tissues per individual, and imposes strong inductive biases to leverage the shared regulatory architecture of tissues and genes. In performance comparison on Genotype-Tissue Expression project data, HYFA achieves superior performance over existing methods, especially when multiple reference tissues are available. The HYFA-imputed dataset can be used to identify replicable regulatory genetic variations (eQTLs), with substantial gains over the original incomplete dataset. HYFA can accelerate the effective and scalable integration of tissue and cell-type transcriptome biorepositories.
Identifiers
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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.