R/CancerGram-package.R

In CancerGram: Prediction of Anticancer Peptides

#' Prediction of anticancer peptides
#'
#' Antimicrobial peptides (AMPs) constitute a diverse group of bioactive molecules 
#' that provide multicellular organisms with protection against microorganisms, and 
#' microorganisms with weaponry for competition. Some AMPs can target cancer cells 
#' and they are called anticancer peptides (ACPs). Due to their small size, positive 
#' charge, hydrophobicity and amphipathicity, AMPs and ACPs interact with negatively 
#' charged components of biological membranes. AMPs preferentially permeabilize 
#' microbial membranes, but ACPs additionally target mitochondrial and plasma 
#' membrane of cancer cells. Taking into account the therapeutic potential of ACPs 
#' and millions of deaths due to cancer annually, it is of vital importance to find 
#' new cationic peptides that selectively destroy cancer cells. Therefore, efficient 
#' computational tools for ACP prediction are essential to identify the best ACP 
#' candidates without undertaking expensive experimental studies. CancerGram is 
#' a novel tool that uses stacked random forests and n-gram analysis for prediction
#' of ACPs.
#' 
#' CancerGram is available as R function (\code{\link{predict.cancergram_model}}) 
#' or shiny GUI (\code{\link{CancerGram_gui}}).
#' 
#' CancerGram requires the external package, CancerGramModel, which 
#' contains models necessary to perform the prediction. The model 
#' can be installed using \code{\link{install_CancerGramModel}}
#'
#' @name CancerGram-package
#' @aliases CancerGram-package CancerGram
#' @docType package
#' @importFrom utils menu
#' @author
#' Maintainer: Michal Burdukiewicz <michalburdukiewicz@@gmail.com>
#' @references Burdukiewicz M, Sidorczuk K, Rafacz D, Pietluch F, Bakala M,
#' Slowik J, Gagat P. (2020) \emph{CancerGram: an effective classifier for 
#' differentiating anticancer from antimicrobial peptides}. (submitted)
#' @keywords package
NULL

if(getRversion() >= "2.15.1")
  utils::globalVariables(c("amp_n_peptide", "neg_n_peptide"))