Gene Embedding Hub: a standardized resource for human gene representations
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Gene embeddings are vector representations of gene identity that provide machine-learning models with information from diverse biological sources. However, their use is hindered by a lack of standardization: inconsistent identifiers, formats, and preprocessing conventions. In this work, we present a unified resource of 67 human gene-embedding sets derived from protein, DNA and RNA sequences, biomedical text, bulk expression data, single-cell expression models, functional data, biological networks, knowledge graphs, regulatory genomics and multimodal models, covering up to 20,097 GENCODE-annotated protein-coding genes. The collection includes both published precomputed embeddings and representations generated or extracted from publicly available models and biological data. Each embedding set is organized in a folder containing the embedding matrix, the Ensembl gene identifiers, machine-readable metadata, and processing documentation. We validate the resource by measuring global and local similarity, cross-embedding consensus, and cross-embedding predictability. The collection is designed to allow community-contributed embeddings that meet predefined technical and documentation requirements, enables comparison of gene representations across modalities and provides standardized embeddings for training and downstream applications.
Article activity feed
-
Authors published a paper in Biorxiv, they used biomaRt in the study. Including #RRIDs will make this less ambiguous. SciScore made a table with this resource, see “Automated Services” module (download as csv, xml or #jats) #STMpublishing #methodsmatter
-
Which gene embedding should you use for your model? We compiled: 🧬 67 human gene embedding sets across 10+ modalities 📐 Aligned to up to 20,097 protein-coding genes with Ensembl IDs 🤗 Ready to load @Hugging Face: huggingface.co/spaces/Boeva... Check out our preprint: www.biorxiv.org/content/10.6...
-
Gene Embedding Hub: a standardized resource for human gene representations #SingleCell 🧪🧬🖥️ https://www.biorxiv.org/content/10.64898/2026.10.29.762818v1
-
Gene Embedding Hub: a standardized resource for human gene representations https://www.biorxiv.org/content/10.64898/2026.10.29.762818v1
-
Gene Embedding Hub: a standardized resource for human gene representations https://www.biorxiv.org/content/10.64898/2026.10.29.762818v1
-
