Gene Embedding Hub: a standardized resource for human gene representations

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.