HPiP
Host-Pathogen Interaction Prediction
Bioconductor version: 3.23 · Package version: 1.18.0
HPiP (Host-Pathogen Interaction Prediction) uses an ensemble learning algorithm for prediction of host-pathogen protein-protein interactions (HP-PPIs) using structural and physicochemical descriptors computed from amino acid-composition of host and pathogen proteins.The proposed package can effectively address data shortages and data unavailability for HP-PPI network reconstructions. Moreover, establishing computational frameworks in that regard will reveal mechanistic insights into infectious diseases and suggest potential HP-PPI targets, thus narrowing down the range of possible candidates for subsequent wet-lab experimental validations.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("HPiP") Details
| Maintainer | Matineh Rahmatbakhsh <matinerb.94@gmail.com> |
| Author | Matineh Rahmatbakhsh [aut, trl, cre], Mohan Babu [led] |
| License | MIT + file LICENSE |
| URL | https://github.com/mrbakhsh/HPiP |
| Bug Reports | https://github.com/mrbakhsh/HPiP/issues |
| Downloads rank | 212 |
| Source branch | RELEASE_3_23 |
| biocViews | GenePrediction, Network, NetworkInference, Proteomics, Software, StructuralPrediction, SystemsBiology |
Documentation
Download
Dependencies
Depends: R (>= 4.1)
Imports: dplyr (>= 1.0.6), httr (>= 1.4.2), readr, tidyr, tibble, utils, stringr, magrittr, caret, corrplot, ggplot2, pROC, PRROC, igraph, graphics, stats, purrr, grDevices, protr, MCL
Suggests: rmarkdown, colorspace, e1071, kernlab, ranger, SummarizedExperiment, Biostrings, randomForest, gprofiler2, gridExtra, ggthemes, BiocStyle, BiocGenerics, RUnit, tools, knitr