{"id":22096201,"url":"https://github.com/aspirincode/stackppi","last_synced_at":"2025-03-24T00:56:43.079Z","repository":{"id":92050369,"uuid":"202877691","full_name":"AspirinCode/StackPPI","owner":"AspirinCode","description":"Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier","archived":false,"fork":false,"pushed_at":"2019-08-11T14:02:04.000Z","size":13620,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-29T07:30:30.880Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":false,"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/AspirinCode.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-08-17T12:23:40.000Z","updated_at":"2024-04-03T13:18:00.000Z","dependencies_parsed_at":null,"dependency_job_id":"eec44e38-79e1-4a79-a320-3a90f7cab72a","html_url":"https://github.com/AspirinCode/StackPPI","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/AspirinCode%2FStackPPI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AspirinCode%2FStackPPI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AspirinCode%2FStackPPI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AspirinCode%2FStackPPI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AspirinCode","download_url":"https://codeload.github.com/AspirinCode/StackPPI/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245191636,"owners_count":20575248,"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":[],"created_at":"2024-12-01T04:09:51.987Z","updated_at":"2025-03-24T00:56:43.073Z","avatar_url":"https://github.com/AspirinCode.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"##StackPPI\n\nImproving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier.\n\n###GcForest-PPI uses the following dependencies:\n* python 3.6 \n* numpy\n* scipy\n* scikit-learn\n\n###Guiding principles:\n\n**The dataset file contains the S. cerevisiae, H. pylori, the independent dataset and network dataset.\n\n**Feature extraction\n1) Evolutionary information: \n   Evolutionary_information.py is the implementation of AAC-PSSM and Bi-PSSM. \n2) PseAAC.m is the implementation of PseAAC.\n3) CTDC.py, CTDT.py, CTDD.py are the implementation of CTD.\n4) Auto_yeast.m is the implementation of AD.\n  \n** Dimensional reduction:\n   XGBoost.py represents XGBoost feature selection\n   stacking_KPCA.py represents KPCA.\n   stacking_LLE.py represents LLE.\n   stacking_TSVD.py represents SVD.\n   stacking_MDS.py represents MDS.\n\n** Classifier:\n   stacking_test.py is the implementation of the stacked ensemble classifier.\n   yeast_Ad.py is the implementation of AdaBoost.\n   yeast_KNN.py is the implementation of KNN.\n   yeast_LR.py is the implementation of LR.\n   yeast_RF.py is the implementation of RF.\n   yeast_SVM.py is the implementation of SVM.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faspirincode%2Fstackppi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faspirincode%2Fstackppi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faspirincode%2Fstackppi/lists"}