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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
mlruns/
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
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logs/

# Unit test / coverage reports
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coverage.xml
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# Translations
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# Django stuff:
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# Flask stuff:
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# Scrapy stuff:
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# PyBuilder
target/

# Jupyter Notebook
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# IPython
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# pyenv
.python-version

# pipenv
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock

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__pypackages__/

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121 changes: 121 additions & 0 deletions README.md
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# Confirmed Variant Recommendation System (ASC3)
Disease similarity (22-3Q-OKR13)
: API to calcualting disease similarity to server GEBRA webpage using TCP socket (https://gebra.io). API requires three data modal including ACMG baysian score, semantic simiarity based on HPO ontology, and etc With these scores, model based on linear regression return disesase similartiy score.


## Contents
- `/ASC3`: 변이추천시스템의 구동 폴더
- `/ASC3/__main__.py`: ASC3 폴더를 동작시기 위한 최상위 코드 (app.py을 구동)
- `/ASC3/app.py`: FastAPI을 동작시키는 메인 모듈
- `/ASC3/models.py`: 쿼리 변이들과 변이들의 특징값을 인자로하여 컨펌 변이의 확률을 추론하는 추론기
- `/ASC3/utils.py`: 로깅과 YAML파일을 로딩하는 유틸
- `/ASC3/logs`: 로그 폴더
- `/validation`: Validation 샘플과 모델 서치를 하기 위한 폴더
- `/anaylsis_code`: 통계량 분석을 위한 ipynb을 담고 있는 폴더
```
.
├── ASC3
│ ├── __main__.py
│ ├── app.py
│ ├── models.py
│ ├── config.yaml
│ ├── routers.py
│ ├── utils.py
│ ├── logs
│ └── tests
├── anaylsis_code
├── backup
├── data
│ └── validation_samples
├── README.md
├── requirements.txt
└── validation
├── config.yaml
├── mlruns
├── model_search_with_mlflow.py
├── tests
├── validation_samples_generator.py
├── validation_utils.py
└── validator.py
```


## Requirement
1. PyYAML
2. locust
3. Python3.7 ~
4. sklearn >= 0.24.2

## Install
TODO


## How to use?
For server (Linux)
```
$ python3 ASC3
```

For client
```
curl -X 'POST' \
'https://127.0.0.1:8000/predict' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"sample_id": "TEST_SAMPLE",
"inhouse_total_ac": 100,
"gene_diseases": {
"OMIM:600276-OMIM:125310": {
"1-100-A-T": {
"acmg_bayesian": 1,
"qual": 1271.77,
"ad": "287.27",
"dp": 129.0,
"disease_similarity": 3.09534946,
"inhouse_variant_ac": 237
},
"1-200-A-T": {
"acmg_bayesian": 0.99409,
"qual": 1271.77,
"ad": "287.27",
"dp": 129.0,
"disease_similarity": 5.09534946,
"inhouse_variant_ac": 3
}
},
"OMIM:12367-OMIM:23223": {
"3-100-A-T": {
"acmg_bayesian": 0.99409,
"qual": 1271.77,
"ad": "287.27",
"dp": 129.0,
"disease_similarity": -1.09534946,
"inhouse_variant_ac": 237
},
"3-200-A-T": {
"acmg_bayesian": 0.99409,
"qual": 1271.77,
"ad": "287.27",
"dp": 129.0,
"disease_similarity": -1.09534946,
"inhouse_variant_ac": 237
}
}
}
}
'
```


## Credit
- Heon
- Isaac
- Tyler
- Kyle

Reviwed by seom, jame


## License
todo
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