{"id":24528962,"url":"https://github.com/saezlab/macau_synergy_prediction","last_synced_at":"2025-06-13T11:33:35.645Z","repository":{"id":77872785,"uuid":"151869580","full_name":"saezlab/Macau_Synergy_Prediction","owner":"saezlab","description":"Target functional similarity based workflows for drug synergy prediction and stratification","archived":false,"fork":false,"pushed_at":"2019-03-20T14:45:54.000Z","size":60230,"stargazers_count":1,"open_issues_count":0,"forks_count":3,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-01-22T07:37:29.260Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/saezlab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","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":"2018-10-06T18:26:31.000Z","updated_at":"2023-02-03T21:15:39.000Z","dependencies_parsed_at":"2023-03-05T13:45:22.542Z","dependency_job_id":null,"html_url":"https://github.com/saezlab/Macau_Synergy_Prediction","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/saezlab%2FMacau_Synergy_Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2FMacau_Synergy_Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2FMacau_Synergy_Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2FMacau_Synergy_Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/saezlab","download_url":"https://codeload.github.com/saezlab/Macau_Synergy_Prediction/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243769947,"owners_count":20345215,"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":"2025-01-22T07:34:12.813Z","updated_at":"2025-03-15T17:44:54.900Z","avatar_url":"https://github.com/saezlab.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Stratification and prediction of drug synergy based on target functional similarity\n\nThe first step is to generate the target-pathway interactions using drug response data on cancer cell lines. Those interaction matrices are generated using codes from this publication: \n\n**Yang et al. Linking drug target and pathway activation for effective therapy using multi-task learning.**\n\nhttps://www.nature.com/articles/s41598-018-25947-y\n\nhttps://github.com/saezlab/Macau_project_1\n\n\n![Alt text](https://github.com/saezlab/Macau_Synergy_Prediction/blob/master/image/Figure_1.png)\n\n\n## Result analysis\n\n**GDSC_DRUG_COMBO_TOP_HITS.Rmd**: \n\n * Identify key pathways for synergy stratification for breast tissue. \n\n * Identify protein target to combine with BRAF for colorectal cancer validation.\n\n * Save target functional similarity values for breast, colon and lung_NSCLC from GDSC dataset.\n\n\n**Use the key pathways to compute the Delta Pathway Activity (predicted synergy) and stratify new cell lines.** \n\n**check_synergy_AZ.Rmd**: \n\n * Synergy tratification analysis for breast/colon/lung cancer cell lines on AstraZeneca dataset. \n\n * Synergy prediction on AstraZeneca dataset. We show here that synergy arises in case of strong similarity or anti-similarity for breast and colorectal tissues.\n\n**check_synergy_SANGER.Rmd**: \n\n * Synergy stratification on 48 colorectal cancer cell lines (Sanger validation).\n \n**check_synergy_ALMANAC.Rmd**: \n\n * Synergy enrichment in NCI_ALMANAC dataset.\n\n## Source of the data\n\nGDSC data were downloaded from: http://www.cancerrxgene.org/\n * Drug IC50 version 17a\n * Basal gene expression 12/06/2013 version 2\n * Drug target version March 2017\n\nDREAM drug combination challenge data were acquired through an AstraZeneca Open Innovation Proposal.\n\nNCI-ALMANAC data is downloaded from the publication Holbeck et al. \n\n## Snapshot of all R packages used for data analysis \n\nIn case you encounter any issue, all packages used in this project are saved in the folder **packrat/src** \n\nhttps://rstudio.github.io/packrat/commands.html\n\n\n## License\n\nDistributed under the GNU GPLv3 License. See accompanying file LICENSE.txt or copy at http://www.gnu.org/licenses/gpl-3.0.html.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaezlab%2Fmacau_synergy_prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaezlab%2Fmacau_synergy_prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaezlab%2Fmacau_synergy_prediction/lists"}