Package: GenProSeq 1.9.0
Dongmin Jung
GenProSeq: Generating Protein Sequences with Deep Generative Models
Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. Machine learning has enabled us to generate useful protein sequences on a variety of scales. Generative models are machine learning methods which seek to model the distribution underlying the data, allowing for the generation of novel samples with similar properties to those on which the model was trained. Generative models of proteins can learn biologically meaningful representations helpful for a variety of downstream tasks. Furthermore, they can learn to generate protein sequences that have not been observed before and to assign higher probability to protein sequences that satisfy desired criteria. In this package, common deep generative models for protein sequences, such as variational autoencoder (VAE), generative adversarial networks (GAN), and autoregressive models are available. In the VAE and GAN, the Word2vec is used for embedding. The transformer encoder is applied to protein sequences for the autoregressive model.
Authors:
GenProSeq_1.9.0.tar.gz
GenProSeq_1.9.0.zip(r-4.5)GenProSeq_1.9.0.zip(r-4.4)GenProSeq_1.9.0.zip(r-4.3)
GenProSeq_1.9.0.tgz(r-4.4-any)GenProSeq_1.9.0.tgz(r-4.3-any)
GenProSeq_1.9.0.tar.gz(r-4.5-noble)GenProSeq_1.9.0.tar.gz(r-4.4-noble)
GenProSeq_1.9.0.tgz(r-4.4-emscripten)GenProSeq_1.9.0.tgz(r-4.3-emscripten)
GenProSeq.pdf |GenProSeq.html✨
GenProSeq/json (API)
NEWS
| # Install 'GenProSeq' in R: |
| install.packages('GenProSeq', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
- example_PTEN - Example Data for Protein Sequences
- example_luxA - Example Data for Protein Sequences
On BioConductor:GenProSeq-1.9.0(bioc 3.20)GenProSeq-1.8.0(bioc 3.19)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 months agofrom:8049af5980
Exports:fit_ARTfit_GANfit_VAEgen_ARTgen_GANgen_VAElayer_embedding_token_positionlayer_transformer_encoderprot_seq_checkprot2vecvec2prot
Dependencies:askpassbackportsbase64encBHbitbit64bslibcachemCatEncodersclicliprcolorspaceconfigcpp11crayoncurldata.tabledata.treeDeepPINCSDiagrammeRdigestdplyrdttenglishevaluatefansifarverfastmapfastmatchfingerprintfloatfontawesomefsgenericsglueherehighrhmshtmltoolshtmlwidgetshttrhunspelligraphISOcodesiteratorsitertoolsjquerylibjsonlitekerasknitrkoRpuskoRpus.lang.enlabelinglatticelexiconlgrlifecyclemagrittrmatlabMatrixMatrixExtramclustmemoisemgsubmimemlapimunsellNLPopensslpillarpkgconfigpngprettyunitsprocessxprogressPRROCpspurrrqdapRegexquantedaR6rappdirsrcdkrcdklibsRColorBrewerRcppRcppArmadilloRcppProgressRcppTOMLreadrreticulateRhpcBLASctlrJavarlangrmarkdownrprojrootrsparserstudioapirvestsassscalesselectrslamSnowballCstopwordsstringdiststringistringrsyllysylly.ensyssyuzhettensorflowtext2vectextcleantextshapetextstemtfautographtfrunstibbletidyrtidyselecttinytextmtokenizersttgseatzdbutf8vctrsviridisLitevisNetworkvroomwebchemwhiskerwithrword2vecxfunxml2yamlzeallotzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Autoregressive language model with Transformer | fit_ART gen_ART |
| Example Data for Protein Sequences | example_luxA |
| Example Data for Protein Sequences | example_PTEN |
| Generative adversarial network for generating protein sequences | fit_GAN gen_GAN |
| Check a protein sequence | prot_seq_check |
| Converting from protein sequences to vectors or vice versa. | prot2vec vec2prot |
| Transformer model | layer_embedding_token_position layer_transformer_encoder |
| Variational autoencoder for generating protein sequences | fit_VAE gen_VAE |
