Package: immApex
Title: Tools for Adaptive Immune Receptor Sequence-Based Machine and
        Deep Learning
Version: 1.2.5
Authors@R: c(
    person(given = "Nick", family = "Borcherding", role = c("aut", "cre"), email = "ncborch@gmail.com"))
Description: A set of tools to for machine and deep learning in R from amino acid and nucleotide sequences focusing on adaptive immune receptors. The package includes pre-processing of sequences, unifying gene nomenclature usage, encoding sequences, and combining models. This package will serve as the basis of future immune receptor sequence functions/packages/models compatible with the scRepertoire ecosystem.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.2
biocViews: Software, ImmunoOncology, SingleCell, Classification,
        Annotation, Sequencing, MotifAnnotation
Depends: R (>= 4.3.0)
Imports: basilisk, hash, httr, keras3, magrittr, matrixStats, methods,
        rvest, SingleCellExperiment, stats, stringi, stringr,
        tensorflow, utils
Suggests: BiocStyle, ggraph, ggplot2, knitr, ggraph, rmarkdown,
        scRepertoire, spelling, testthat, tidygraph, viridis
SystemRequirements: Python (via basilisk)
VignetteBuilder: knitr
Language: en-US
URL: https://github.com/BorchLab/immApex/
BugReports: https://github.com/BorchLab/immApex/issues
git_url: https://git.bioconductor.org/packages/immApex
git_branch: RELEASE_3_21
git_last_commit: 70313fe
git_last_commit_date: 2025-06-24
Repository: Bioconductor 3.21
Date/Publication: 2025-06-29
NeedsCompilation: no
Packaged: 2025-06-29 21:47:26 UTC; biocbuild
Author: Nick Borcherding [aut, cre]
Maintainer: Nick Borcherding <ncborch@gmail.com>
