This repository consists of python scripts, jupyter notebooks, R scripts, datasets, source codes, figures, evaluation metrics of the network which are created or obtained for this project.
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proposed_model <- Project folder
│
├── data
│ ├── EXPERIMENT <- The index information
│ ├── geneSCF <- Pathway information from geneSCF platform in https://github.com/genescf
│ ├── pathways <- The details of pathway information
│ └── weights <- The prior biological knowledge which includes into first hidden layer
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├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ and a short `-` delimited description, e.g.
│ `1.0-initial-data-exploration`
│
├── source <- External data sources
│ └── README.md <- The explanation of data source
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├── third_party <- Source code from reference papers
│ ├── PMC5737331 <- Reference paper code details
│ └── third_party.txt <- reference paper link information
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├── README.md <- Project details
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├── retrieval_analysis.sh <- retrieval analysis
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└── tgpu.yml <- Ptyhon environment
Project based on the cookiecutter data science project template. #cookiecutterdatascience
=================================================- Create environment
... $ conda env create -f tgpu.yml
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To execute R and Pyhon script in notebooks folder
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To perform retrival analysis by
.../proposed_model$ ./ retrieval_analysis.sh
- Retrieval Analysis Result
.../proposed_model$ python notebooks/load_retrieval_summary.py retrieval_analysis
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no 813533.
More detail in MLFPM webpage