{"id":21023862,"url":"https://github.com/biogenies/cancergram","last_synced_at":"2026-03-03T07:32:23.572Z","repository":{"id":56935430,"uuid":"300318252","full_name":"BioGenies/CancerGram","owner":"BioGenies","description":"Predicts anticancer peptides using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI.","archived":false,"fork":false,"pushed_at":"2020-11-19T20:31:32.000Z","size":1815,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-05-10T03:08:55.267Z","etag":null,"topics":["anticancer-peptides","bioinformatics","k-mer","n-gram","peptide-identification","r-package","random-forests"],"latest_commit_sha":null,"homepage":"https://biogenies.github.io/CancerGram/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/BioGenies.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-10-01T14:57:04.000Z","updated_at":"2022-02-14T02:43:16.000Z","dependencies_parsed_at":"2022-08-21T05:50:12.007Z","dependency_job_id":null,"html_url":"https://github.com/BioGenies/CancerGram","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BioGenies%2FCancerGram","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BioGenies%2FCancerGram/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BioGenies%2FCancerGram/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BioGenies%2FCancerGram/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BioGenies","download_url":"https://codeload.github.com/BioGenies/CancerGram/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254304667,"owners_count":22048447,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["anticancer-peptides","bioinformatics","k-mer","n-gram","peptide-identification","r-package","random-forests"],"created_at":"2024-11-19T11:20:14.339Z","updated_at":"2026-03-03T07:32:23.543Z","avatar_url":"https://github.com/BioGenies.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![published in: Pharmaceutics](https://img.shields.io/badge/published%20in-Pharmaceutics-green.svg)](https://doi.org/10.3390/pharmaceutics12111045)\n[![R build status](https://github.com/BioGenies/CancerGram/workflows/R-CMD-check/badge.svg)](https://github.com/BioGenies/CancerGram/actions)\n[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/CancerGram)](https://cran.r-project.org/package=CancerGram)\n[![Downloads](http://cranlogs.r-pkg.org/badges/CancerGram)](https://cran.r-project.org/package=CancerGram)\n\nIdentify anticancer peptides\n-------------------------\n\nCancerGram identifies anticancer peptides using n-gram encoding and random forests. It can be also accessed as a web-based service http://biongram.biotech.uni.wroc.pl/CancerGram/. \n\nHow to cite?\n-------------------------\nPlease use: Burdukiewicz, M., Sidorczuk, K., Rafacz, D., Pietluch, F., Bąkała, M., Słowik, J., and Gagat, P. (2020). CancerGram: An Effective Classifier for Differentiating Anticancer from Antimicrobial Peptides. Pharmaceutics 12, 1045, https://doi.org/10.3390/pharmaceutics12111045.\n\nLocal instance of CancerGram\n------------------------\n\nYou can install the latest development version of the package:\n\n```R\nsource(\"https://raw.githubusercontent.com/r-lib/remotes/master/install-github.R\")$value(\"BioGenies/CancerGram\")\n```\n\nAfter installation GUI can be accessed locally:\n\n```R\nlibrary(CancerGram)\nCancerGram_gui()\n```\n\n\nInstalling dependency: CancerGramModel\n------------------------\nTo be able to use CancerGram properly, you should have installed the 'CancerGramModel' package available via GitHub. \nCancerGramModel contains stacked random forest model and informative n-grams required for prediction of anticancer peptides.\nDue to the large size of a model, it needs to be stored in the external repository, as CRAN do not allow upload of files\nlarger than 5 MB. \n\nYou can install CancerGramModel using the install_CancerGramModel function:\n\n```R\ninstall_CancerGramModel()\n```\n\nAnticancer peptides might be also identified in the batch mode:\n\n```R\nlibrary(CancerGram)\nlibrary(CancerGramModel)\n# if you do not have CancerGramModel use:\n# install_CancerGramModel()\nsequences \u003c- read_txt(system.file(\"CancerGram/prots.txt\", package = \"CancerGram\"))\npredict(CancerGram_model, sequences)\n```\n\nUnix/macOS: curl\n------------------------\n\nThe curl library is one of the dependencies of the CancerGram package and requires additional, non-R software. If you encounter an error concerning curl, please follow instructions below to install curl (adapted from https://github.com/jeroen/curl).\n\nBinary packages for OS-X or Windows can be installed directly from CRAN:\n\n```r\ninstall.packages(\"curl\")\n```\n\nInstallation from source on Linux requires `libcurl`. On Debian or Ubuntu use libcurl4-openssl-dev:\n\n```bash\nsudo apt-get install -y libcurl-dev\n```\n\nOn Fedora, CentOS or RHEL use libcurl-devel:\n\n```bash\nsudo yum install libcurl-devel\n```\n\nOn OS-X libcurl is included with the system so nothing extra is needed. However if you want to build against the most recent version of libcurl, install and force-link curl from homebrew:\n\n```bash\nbrew install curl\nbrew link --force curl\n```\n\nNote that on OS-X you must recompile the R package from source after force-linking curl, otherwise you get a version conflict with the system version of libcurl.\n\nFunding\n------------------------\nThis work was supported by National Science Centre grants 2017/26/D/NZ8/00444, 2018/31/N/NZ2/01338 and 2019/35/N/NZ8/03366 to FP.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiogenies%2Fcancergram","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbiogenies%2Fcancergram","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiogenies%2Fcancergram/lists"}