Package: DeProViR 1.1.0
Matineh Rahmatbakhsh
DeProViR: A Deep-Learning Framework Based on Pre-trained Sequence Embeddings for Predicting Host-Viral Protein-Protein Interactions
Emerging infectious diseases, exemplified by the zoonotic COVID-19 pandemic caused by SARS-CoV-2, are grave global threats. Understanding protein-protein interactions (PPIs) between host and viral proteins is essential for therapeutic targets and insights into pathogen replication and immune evasion. While experimental methods like yeast two-hybrid screening and mass spectrometry provide valuable insights, they are hindered by experimental noise and costs, yielding incomplete interaction maps. Computational models, notably DeProViR, predict PPIs from amino acid sequences, incorporating semantic information with GloVe embeddings. DeProViR employs a Siamese neural network, integrating convolutional and Bi-LSTM networks to enhance accuracy. It overcomes the limitations of feature engineering, offering an efficient means to predict host-virus interactions, which holds promise for antiviral therapies and advancing our understanding of infectious diseases.
Authors:
DeProViR_1.1.0.tar.gz
DeProViR_1.1.0.zip(r-4.5)DeProViR_1.1.0.zip(r-4.4)DeProViR_1.1.0.zip(r-4.3)
DeProViR_1.1.0.tgz(r-4.4-any)DeProViR_1.1.0.tgz(r-4.3-any)
DeProViR_1.1.0.tar.gz(r-4.5-noble)DeProViR_1.1.0.tar.gz(r-4.4-noble)
DeProViR_1.1.0.tgz(r-4.4-emscripten)DeProViR_1.1.0.tgz(r-4.3-emscripten)
DeProViR.pdf |DeProViR.html✨
DeProViR/json (API)
NEWS
| # Install 'DeProViR' in R: |
| install.packages('DeProViR', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mrbakhsh/deprovir/issues
On BioConductor:DeProViR-1.1.0(bioc 3.20)DeProViR-1.0.0(bioc 3.19)
Last updated 2 months agofrom:4c1b40bce6
Exports:encodeHostSeqencodeViralSeqgloveImportloadPreTrainedModelloadTrainingSetmodelTrainingperformancePlotspredInteractions
Dependencies:askpassbackportsbase64encBiocFileCachebitbit64blobcachemcaretclassclicliprclockcodetoolscolorspaceconfigcpp11crayoncurldata.tableDBIdbplyrdiagramdigestdplyre1071ellipsisfansifarverfastmapfilelockfmsbforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatherehmshttripredisobanditeratorsjsonlitekerasKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmemoisemgcvmimeModelMetricsmunsellnlmennetnumDerivopensslparallellypillarpkgconfigplogrplyrpngprettyunitspROCprocessxprodlimprogressprogressrproxyPRROCpspurrrR6rappdirsRColorBrewerRcppRcppTOMLreadrrecipesreshape2reticulaterlangrpartrprojrootRSQLiterstudioapiscalesshapeSQUAREMstringistringrsurvivalsystensorflowtfautographtfrunstibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitevroomwhiskerwithryamlzeallot
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Host Protein Sequence Encoding with GloVe Embedding Vectors | encodeHostSeq |
| Viral Protein Sequence Encoding with GloVe Embedding Vectors | encodeViralSeq |
| Cache and Load Pre-Trained Word Vectors | gloveImport |
| Load Pre-Trained Model Weights | loadPreTrainedModel |
| Load Demo Training Set | loadTrainingSet |
| Predictive Model Training using k-fold Validation Strategy | modelTraining |
| Model Performance Evalution | performancePlots |
| Predict Unknown Interactions | predInteractions |
