{ "_id": "6a101b5bacfb0bcc41c870c5", "Package": "MantaID", "Title": "A Machine-Learning Based Tool to Automate the Identification of\nBiological Database IDs", "Version": "1.0.4", "Authors@R": "c(person(\"Zhengpeng\", \"Zeng\",email = \"molaison@foxmail.com\", role = c(\"aut\",\"cre\",\"ctb\"),comment = c(ORCID = \"0000-0001-7919-209X\")),\nperson(\"Longfei\",\"Mao\",email = \"longfeimao99@gmail.com\",role = c(\"aut\",\"cph\"),comment = c(ORCID = \"0000-0003-0759-0501\")),\nperson(\"Feng\",\"Yu\",email = \"feng_yu@hnu.edu.cn\",role = c(\"aut\"), comment = c(ORCID = \"0000-0002-5221-281X\")),\nperson(\"Jiamin\",\"Hu\",email = \"Jiamin-Hu@hotmail.com\",role = c(\"ctb\"), comment = c(ORCID = \"0000-0003-3030-2117\")),\nperson(\"Xiting\",\"Wang\",email = \"XitingWang2023@outlook.com\",role = c(\"ctb\"), comment = c(ORCID = \"0009-0009-5235-0006\")))", "Description": "The number of biological databases is growing rapidly, but\ndifferent databases use different IDs to refer to the same\nbiological entity. The inconsistency in IDs impedes the\nintegration of various types of biological data. To resolve the\nproblem, we developed 'MantaID', a data-driven,\nmachine-learning based approach that automates identifying IDs\non a large scale. The 'MantaID' model's prediction accuracy was\nproven to be 99%, and it correctly and effectively predicted\n100,000 ID entries within two minutes. 'MantaID' supports the\ndiscovery and exploitation of ID patterns from large quantities\nof databases. (e.g., up to 542 biological databases). An\neasy-to-use freely available open-source software R package, a\nuser-friendly web application, and API were also developed for\n'MantaID' to improve applicability. To our knowledge, 'MantaID'\nis the first tool that enables an automatic, quick, accurate,\nand comprehensive identification of large quantities of IDs,\nand can therefore be used as a starting point to facilitate the\ncomplex assimilation and aggregation of biological data across\ndiverse databases.", "License": "GPL (>= 3)", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.2", "LazyData": "true", "URL": "https://molaison.github.io/MantaID/", "Config/pak/sysreqs": "cmake libglpk-dev make libicu-dev libpng-dev\nlibxml2-dev libssl-dev python3 zlib1g-dev", "Repository": "https://molaison.r-universe.dev", "Date/Publication": "2024-09-18 09:16:30 UTC", "RemoteUrl": "https://github.com/molaison/mantaid", "RemoteRef": "HEAD", "RemoteSha": "1b1adb3c7aca9749cd601f43640fd9fa4e10a67a", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-14 09:18:16 UTC", "User": "root" }, "Author": "Zhengpeng Zeng [aut, cre, ctb] (ORCID:\n),\nLongfei Mao [aut, cph] (ORCID: ),\nFeng Yu [aut] (ORCID: ),\nJiamin Hu [ctb] (ORCID: ),\nXiting Wang [ctb] (ORCID: )", "Maintainer": "Zhengpeng Zeng ", "MD5sum": "c3cfec614916629476b0f3c6fc3f46af", "_user": "molaison", "_type": "src", "_file": "MantaID_1.0.4.tar.gz", "_fileid": "405578957aea5c7de9c147b3c829429a1881b492c5eb0bc5328e21119aaefc5b", "_filesize": 76660196, "_sha256": "405578957aea5c7de9c147b3c829429a1881b492c5eb0bc5328e21119aaefc5b", "_created": "2026-05-14T09:18:16.000Z", "_published": "2026-05-22T09:01:15.921Z", "_distro": "noble", "_jobs": [ { "job": 77348363105, "time": 282, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "6991243215" }, { "job": 77348362924, "time": 291, "config": "linux-release-x86_64", "r": "4.6.0", "check": "NOTE", "artifact": "6991245712" }, { "job": 77348362722, "time": 255, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "NOTE", "artifact": "6991228367" }, { "job": 77348363094, "time": 194, "config": "macos-release-arm64", "r": "4.6.0", 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"_maintainer": { "name": "Zhengpeng Zeng", "email": "molaison@foxmail.com", "orcid": "0000-0001-7919-209X" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.4.0", "role": "Depends" }, { "package": "biomaRt", "role": "Depends" }, { "package": "xgboost", "role": "Depends" }, { "package": "caret", "role": "Depends" }, { "package": "data.table", "role": "Depends" }, { "package": "dplyr", "role": "Depends" }, { "package": "ggplot2", "role": "Depends" }, { "package": "keras", "role": "Depends" }, { "package": "magrittr", "role": "Depends" }, { "package": "mlr3tuning", "role": "Depends" }, { "package": "mlr3", "role": "Depends" }, { "package": "mlr3learners", "role": "Depends" }, { "package": "purrr", "role": "Depends" }, { "package": "reshape2", "role": "Depends" }, { "package": "scutr", "role": "Depends" }, { "package": "stringr", "role": "Depends" }, { "package": "tibble", "role": "Depends" }, { "package": "tidyr", "role": "Depends" }, { "package": "tidyselect", 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"page": "Example", "title": "ID example dataset.", "topics": [ "Example" ] }, { "page": "mi", "title": "A wrapper function that executes MantaID workflow.", "topics": [ "mi" ] }, { "page": "mi_balance_data", "title": "Data balance. Most classes adopt random undersampling, while a few classes adopt smote method to oversample to obtain relatively balanced data;", "topics": [ "mi_balance_data" ] }, { "page": "mi_clean_data", "title": "Reshape data and delete meaningless rows.", "topics": [ "mi_clean_data" ] }, { "page": "mi_data_attributes", "title": "ID-related datasets in biomart.", "topics": [ "mi_data_attributes" ] }, { "page": "mi_data_procID", "title": "Processed ID data.", "topics": [ "mi_data_procID" ] }, { "page": "mi_data_rawID", "title": "ID dataset for testing.", "topics": [ "mi_data_rawID" ] }, { "page": "mi_filter_feat", "title": "Performing feature selection in a automatic way based on correlation and feature importance.", "topics": [ "mi_filter_feat" ] }, { "page": "mi_get_confusion", "title": "Compute the confusion matrix for the predicted result.", "topics": [ "mi_get_confusion" ] }, { "page": "mi_get_ID", "title": "Get ID data from the 'Biomart' database using 'attributes'.", "topics": [ 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