Gene Embedding Hub: a standardized resource for human gene representations
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,…
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