Gene embeddings used in GenePT: A Simple But Hard-to-Beat Foundation Model for Genes and Cells Built From ChatGPT

<p>These are the pulled NCBI (and UniProt, when applicable) summaries of genes, as well as the corresponding OpenAI text embeddings (text-embedding-ada-002 and text-embedding-3-large) computed on the summaries. See methods details in Chen and Zou (2024+).</p> <p>The unzipped folder contains four different files:&nbsp;</p> <ol> <li>NCBI_summary_of_genes.json (NCBI gene card summary of human genes)</li> <li>NCBI_UniProt_summary_of_genes.json (NCBI gene card and UniProt protein (when applicable) summary of human genes)</li> <li>GenePT_gene_embedding_ada_text.pickle (a dictionary of numpy array where gene names (upper case) are keys and text-embedding-ada-002 embeddings of the summary in 1. are the values)</li> <li>GenePT_gene_protein_embedding_model_3_text.pickle (a dictionary of numpy array where gene names (upper case) are keys and text-embedding-3-large embeddings of the summary in 1. are the values)</li> </ol> <p>Reference:</p> <p>Chen YT, Zou J. (2024+) GenePT: A Simple But Effective Foundation Model for Genes and Cells Built From ChatGPT. bioRxiv preprint: <a href="https://www.biorxiv.org/content/10.1101/2023.10.16.562533v1">https://www.biorxiv.org/content/10.1101/2023.10.16.562533v1</a>.</p>