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In this package, common deep\ngenerative models for protein sequences, such as variational\nautoencoder (VAE), generative adversarial networks (GAN), and\nautoregressive models are available. In the VAE and GAN, the\nWord2vec is used for embedding. The transformer encoder is\napplied to protein sequences for the autoregressive model.", "Version": "1.17.1", "Date": "2024-02-06", "Authors@R": "c(person(given=\"Dongmin\", family=\"Jung\", email=\"dmdmjung@gmail.com\", role=c(\"cre\", \"aut\"), comment = c(ORCID = \"0000-0001-7499-8422\")))", "LazyData": "FALSE", "License": "Artistic-2.0", "biocViews": "Software, Proteomics", "NeedsCompilation": "no", "VignetteBuilder": "knitr", "Config/Bioconductor/UnsupportedPlatforms": "windows", "Config/pak/sysreqs": "cmake libglpk-dev make default-jdk libicu-dev\nlibpng-dev libuv1-dev libxml2-dev libssl-dev python3 libx11-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-05-29 18:37:14 UTC", "RemoteUrl": "https://github.com/bioc/GenProSeq", "RemoteRef": "HEAD", "RemoteSha": "e67e085a30c6939aa6ac75d3e0ffaf98fb97d7ba", "Packaged": { "Date": "2026-05-29 21:58:20 UTC", "User": "root" }, "Author": "Dongmin Jung [cre, aut] (ORCID:\n)", "Maintainer": "Dongmin Jung ", 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