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. 2022 Sep 29;13:5731. doi: 10.1038/s41467-022-33397-4

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a Data processing and annotation. Assembled contigs from public databases were downloaded and underwent gene calling and annotation. Both annotated and unannotated genes were clustered into gene families. b Distribution of annotated and hypothetical genes in the corpus. The left bar chart represents all ~360 million genes used in the corpus. The right bar chart represents ~560,000 unique gene families (i.e., the corpus “vocabulary”). c A comparison between English and genomic corpora. The “sentences” in the genomic corpus are contigs, which are composed of gene families identifiers as “words”. d Embedding generation and function prediction pipeline. Embeddings (numeric vector representations) are generated by the word2vec algorithm and serve as the input to a deep neural network for gene function classification.