{"id":21441769,"url":"https://github.com/dmis-lab/arpnet","last_synced_at":"2025-03-17T00:43:04.303Z","repository":{"id":97388578,"uuid":"168137232","full_name":"dmis-lab/arpnet","owner":"dmis-lab","description":"ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder","archived":false,"fork":false,"pushed_at":"2019-01-30T14:10:19.000Z","size":174,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-23T10:35:35.225Z","etag":null,"topics":[],"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/dmis-lab.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}},"created_at":"2019-01-29T10:36:30.000Z","updated_at":"2019-10-24T17:07:04.000Z","dependencies_parsed_at":"2023-05-31T09:00:38.632Z","dependency_job_id":null,"html_url":"https://github.com/dmis-lab/arpnet","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/dmis-lab%2Farpnet","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmis-lab%2Farpnet/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmis-lab%2Farpnet/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmis-lab%2Farpnet/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dmis-lab","download_url":"https://codeload.github.com/dmis-lab/arpnet/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243955720,"owners_count":20374372,"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":"2024-11-23T01:42:14.062Z","updated_at":"2025-03-17T00:43:04.285Z","avatar_url":"https://github.com/dmis-lab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ARPNet\n![model image](model_description.PNG)\nThis repository provides implementation code of ARPNet, a Antidepressant Response Prediction Network for Major Depressive Disorder.\n\n## Installation\nThe experiments were conducted on a single TITAN Xp GPU machine which has 12GB of RAM.\nThe implemntation code of ARPNet was tested with the following requirements:\n*   **[`Python2.7.12`](https://www.python.org/downloads/release/python-2712/)**\n*   **[`TensorFlow1.6.0`](https://www.tensorflow.org/install/)**\n\n## Datasets\n### Description\nWe conducted experients on the data of 121 patients with MDD collected from Korea University Anam Hospital, Seoul, Korea. By several feature selection steps, we extracted some useful features, which are demographic, neuroimaging biomarkers, genetic variants, and DNA methylation features, for predicting antidepressant response from patient data. Because of the Korea University Anam Hospital's policies related to the patients' personal information, the data used in the experiment could not be released. \n\n### Toy Smaples\nTo run our implementation code, we release the toy samples `\\toy\\train_1.tsv`, `\\toy\\train_2.tsv`, `\\toy\\test_1.tsv`, `\\toy\\test_2.tsv` , and `\\toy\\patient_data.tsv`\n* `train_1.tsv` and `test_1.tsv` are used for the prediction of the degree of antdepressant response (Task 1). \n* `train_2.tsv` and `test_2.tsv` are used for the prediction of the clinical remession of patients (Task 2).\n* `patient_data.tsv` consists of demographic, neuroimaging biomarkers, genetic variants, and DNA methylation features.\n\n## Run\nFollowing command runs ARPNet code on toy samples with default parameters.\n```\npython run.py \\\n    --task=1\n```\nYou can change the task as you want. Once you perfrom task 2, you can use it in task mode by using `--task=2`\n\n## Citation\n\nIf we submit the paper to a conference or journal, we will update the BibTeX.\n\n## Contact information\n\nFor help or issues using ARPNet, please submit a GitHub issue. Please contact Buru Chang\n(`buru_chag (at) korea.ac.kr`), or Yonghwa Choi (`yonghwachoi (at) korea.ac.kr`) for communication related to ARPNet.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdmis-lab%2Farpnet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdmis-lab%2Farpnet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdmis-lab%2Farpnet/lists"}