{"id":22308632,"url":"https://github.com/sysbiochalmers/dlkcat","last_synced_at":"2025-07-29T06:30:54.555Z","repository":{"id":37582531,"uuid":"265207131","full_name":"SysBioChalmers/DLKcat","owner":"SysBioChalmers","description":"Deep learning and Bayesian approach applied to enzyme turnover number for the improvement of enzyme-constrained genome-scale metabolic models (ecGEMs) reconstruction","archived":false,"fork":false,"pushed_at":"2023-07-04T15:32:14.000Z","size":48467,"stargazers_count":151,"open_issues_count":6,"forks_count":57,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-04-05T09:04:07.912Z","etag":null,"topics":["bayesian","deep-learning","enzyme-constraints","enzyme-turnover-number","kcat","kinetics"],"latest_commit_sha":null,"homepage":"","language":"Python","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/SysBioChalmers.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-05-19T09:43:48.000Z","updated_at":"2025-03-27T13:16:20.000Z","dependencies_parsed_at":"2023-01-30T04:00:50.530Z","dependency_job_id":null,"html_url":"https://github.com/SysBioChalmers/DLKcat","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/SysBioChalmers/DLKcat","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SysBioChalmers%2FDLKcat","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SysBioChalmers%2FDLKcat/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SysBioChalmers%2FDLKcat/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SysBioChalmers%2FDLKcat/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SysBioChalmers","download_url":"https://codeload.github.com/SysBioChalmers/DLKcat/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SysBioChalmers%2FDLKcat/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":267639569,"owners_count":24119780,"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","status":"online","status_checked_at":"2025-07-29T02:00:12.549Z","response_time":2574,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["bayesian","deep-learning","enzyme-constraints","enzyme-turnover-number","kcat","kinetics"],"created_at":"2024-12-03T20:14:46.981Z","updated_at":"2025-07-29T06:30:49.538Z","avatar_url":"https://github.com/SysBioChalmers.png","language":"Python","readme":"DLKcat\n======\n\n\u003cp align=\"center\"\u003e\n  \u003cimg  src=\"doc/logo.png\" width = \"400\"\u003e\n\u003c/p\u003e\n\n\nIntroduction\n------------\n\nThe **DLKcat** toolbox is a Matlab/Python package for prediction of\nkcats and generation of the ecGEMs. The repo is divided into two parts:\n`DeeplearningApproach` and `BayesianApproach`. `DeeplearningApproach`\nsupplies a deep-learning based prediction tool for kcat prediction,\nwhile `BayesianApproach` supplies an automatic Bayesian based pipeline\nto construct ecModels using the predicted kcats.\n\nUsage\n-----\n\n-   Please check the instruction `README` file under these two section\n    `Bayesianapproach` and `DeeplearningApproach` for reporducing all figures in\n    the paper.\n-   For people who are interested in using the trained deep-learning\n    model for their own kcat prediction, we supplied an example. please\n    check usage for **detailed information** in the file\n    [DeeplearningApproach/README](https://github.com/SysBioChalmers/DLKcat/tree/master/DeeplearningApproach)\n    under the `DeeplearningApproach`.\n\n    \u003e -   `input` for the prediction is the `Protein sequence` and\n    \u003e     `Substrate SMILES structure/Substrate name`, please check the\n    \u003e     file in\n    \u003e     [DeeplearningApproach/Code/example/input.tsv](https://github.com/SysBioChalmers/DLKcat/tree/master/DeeplearningApproach/Code/example)\n    \u003e -   `output` is the correponding `kcat` value\n\nCitation\n-----\n\n- Please cite the paper [Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction](https://www.nature.com/articles/s41929-022-00798-z)\"\"\n\n\nNotes\n-------\nWe noticed there is a mismatch of reference list in Supplementary Table 2 of the publication, therefore we made an update for that. New supplementary Tables can be found [here](https://github.com/SysBioChalmers/DLKcat/tree/master/DeeplearningApproach/Results/figures)\n\nContact\n-------\n\n-   Feiran Li ([@feiranl](https://github.com/feiranl)), Chalmers\n    University of Technology, Gothenburg, Sweden\n-   Le Yuan ([@le-yuan](https://github.com/le-yuan)), Chalmers\n    University of Technology, Gothenburg, Sweden\n\nLast update: 2022-04-09\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsysbiochalmers%2Fdlkcat","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsysbiochalmers%2Fdlkcat","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsysbiochalmers%2Fdlkcat/lists"}