{"id":22096241,"url":"https://github.com/aspirincode/lgbm-dtis","last_synced_at":"2026-01-04T08:53:51.064Z","repository":{"id":92049738,"uuid":"323034239","full_name":"AspirinCode/LGBM-DTIs","owner":"AspirinCode","description":"LGBM-DTIs: Predicting drug-target interactions using LightGBM with protein multi-information and molecular structure ","archived":false,"fork":false,"pushed_at":"2020-12-20T05:09:52.000Z","size":1086,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-29T07:30:30.958Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"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":"2020-12-20T09:17:58.000Z","updated_at":"2022-01-19T04:56:11.000Z","dependencies_parsed_at":null,"dependency_job_id":"47380f8c-4a78-4bf1-ae97-f67c416c54a5","html_url":"https://github.com/AspirinCode/LGBM-DTIs","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%2FLGBM-DTIs","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AspirinCode%2FLGBM-DTIs/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AspirinCode%2FLGBM-DTIs/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AspirinCode%2FLGBM-DTIs/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AspirinCode","download_url":"https://codeload.github.com/AspirinCode/LGBM-DTIs/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":"2024-12-01T04:09:53.676Z","updated_at":"2026-01-04T08:53:51.018Z","avatar_url":"https://github.com/AspirinCode.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"##LGBM-DTIs\n\nLGBM-DTIs：Predicting drug-target interactions using LightGBM with protein multi-information and molecular structure.\n\n\n###LGBM-DTIs uses the following dependencies:\n* MATLAB2014a\n* python 3.6 \n* numpy\n* pandas\n* scikit-learn\n* imblearn \n\n\n###Guiding principles:\n\n**The dataset file contains the gold standard dataset, Kuang dataset and network dataset.\n\n**feature extraction:\n1) Evolutionary-based features: PsePSSM.m is the implementation of PsePSSM. \n2) Sequence-based features: PAAC.py is the implementation of PseAAC.\n3) Structural-based features: Structure.py is the implementation of structural information based on SPIDER3.\n   \n** feature selection:\n   Lasso.py represents the Lasso.\n   Elastic_net.py represents the elastic net.\n   ET.py represents the extra trees.\n   IG.py represents the information gain.\n   LLE.py represents the locally linear embedding.\n   MI.py represents the mutual information.\n   \n** data preprocessing:\n   SMOTE.R is the implementation of SMOTE. \n   AllKNN.py is the implementation of AllKNN. \n   ENN.py is the implementation of edited nearest neighbours. \n   OSS.py is the implementation of one-sided selection. \n   RUS.py is the implementation of random undersampling.\n\n** Classifier:\n   LightGBM.py is the implementation of LightGBM.\n   AdaBoost.py is the implementation of AdaBoost.\n   DT.py is the implementation of decision tree. \n   GBM.py is the implementation of gradient boosting machine.\n   KNN.py is the implementation of K-nearest neighbor. \n   LR.py is the implementation of logistic regression. \n   NB.py is the implementation of NB.\n   RF.py is the implementation of random forest. \n   SVM.py is the implementation of support vector machine.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faspirincode%2Flgbm-dtis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faspirincode%2Flgbm-dtis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faspirincode%2Flgbm-dtis/lists"}