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clep-0.0.2
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مشاهده بیشترتوضیحات
A Hybrid Data and Knowledge Driven Framework for Generating Patient Representations
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | clep-0.0.2 |
| نام | clep |
| نسخه کتابخانه | 0.0.2 |
| نگهدارنده | ['Vinay Bharadhwaj'] |
| ایمیل نگهدارنده | ['vinay.srinivas.bharadhwaj@scai.fraunhofer.de'] |
| نویسنده | Vinay Bharadhwaj |
| ایمیل نویسنده | vinay.srinivas.bharadhwaj@scai.fraunhofer.de |
| آدرس صفحه اصلی | https://github.com/hybrid-kg/clep |
| آدرس اینترنتی | https://pypi.org/project/clep/ |
| مجوز | See LICENSE file |
<p align="center">
<img src="docs/source/logo.jpg">
</p>
<h1 align="center">
CLEP: A Hybrid Data- and Knowledge- Driven Framework for Generating Patient Representations
<br/>
<a href='https://travis-ci.com/github/hybrid-kg'>
<img src="https://travis-ci.com/hybrid-kg/clep.svg?branch=master" />
</a>
<a href='https://clep.readthedocs.io/en/latest/?badge=latest'>
<img src='https://readthedocs.org/projects/clep/badge/?version=latest' alt='Documentation Status' />
</a>
<a href="https://zenodo.org/badge/latestdoi/209278408">
<img src="https://zenodo.org/badge/209278408.svg" alt="DOI">
</a>
<a href="https://pypi.org/project/clep/">
<img src="https://img.shields.io/pypi/v/clep" alt="CLEP on PyPI">
</a>
<img src="https://img.shields.io/pypi/pyversions/clep" alt="CLEP Python versions">
<a href="https://github.com/hybrid-kg/clep/blob/master/LICENSE">
<img src="https://img.shields.io/pypi/l/clep" alt="CLEP Software License">
</a>
</h1>
## Table of Contents
* [General Info](#general-info)
* [Installation](#installation)
* [Documentation](#documentation)
* [Input Data](#input-data-formats)
* [Usage](#usage)
* [Issues](#issues)
* [Acknowledgements](#acknowledgements)
* [Citation](#citation)
* [Disclaimer](#disclaimer)
## General Info
CLEP is a framework that contains novel methods for generating patient representations from any patient level data and its corresponding prior knowledge encoded in a knowledge graph. The framework is depicted in the graphic below
<p align="center">
<img src="docs/source/framework.jpg">
</p>
## Installation
The code can be installed from [PyPI](https://pypi.org/project/clep/) with:
```bash
$ pip install clep
```
The most recent code can be installed from the source on [GitHub](https://github.com/hybrid-kg/clep) with:
```bash
$ pip install git+https://github.com/hybrid-kg/clep.git
```
For developers, the repository can be cloned from [GitHub](https://github.com/hybrid-kg/clep) and installed in editable mode with:
```bash
$ git clone https://github.com/hybrid-kg/clep.git
$ cd clep
$ pip install -e .
```
## Documentation
Read the [official docs](https://clep.readthedocs.io/en/latest/) for more information.
## Input Data Formats
### Data
| Symbol | Sample_1 | Sample_2 | Sample_3 |
| ------ | -------- | -------- | -------- |
| HGNC_ID_1 | 0.354 | 2.568 | 1.564 |
| HGNC_ID_2 | 1.255 | 1.232 | 0.26452 |
| HGNC_ID_3 | 3.256 | 1.5 | 1.5462 |
**Note:** The data must be in a tab separated file format.
### Design
| FileName | Target |
| -------- | ------ |
| Sample_1 | Abnormal |
| Sample_2 | Abnormal |
| Sample_3 | Control |
**Note:** The data must be in a tab separated file format.
### Knowledge Graph
The graph format CLEP can handle is a modified version of the Edge List Format. Which looks as follows:
| Source | Relation | Target |
| ------ | -------- | ------ |
| HGNC_ID_1 | association | HGNC_ID_2
| HGNC_ID_2 | decreases | HGNC_ID_3
| HGNC_ID_3 | increases | HGNC_ID_1
**Note:** The data must be in a tab separated file format & if your knowledge graph does not have relations between the source and the target, just populate the relation column with "No Relation".
## Usage
**Note:** These are very basic commands for clep, and the detailed options for each command can be found in the [documentation](#documentation)
1. **Radical Searching**
The following command finds the extreme samples with extreme feature values based on the control population.
```bash
$ clep sample-scoring radical-search --data <DATA_FILE> --design <DESIGN_FILE> --control Control --threshold 2.5 --control_based --ret_summary --out <OUTPUT_DIR>
```
2. **Graph Generation**
The following command generates the patient-gene network based on the method chosen (Interaction_network).
```bash
$ clep embedding generate-network --data <SCORED_DATA_FILE> --method interaction_network --ret_summary --out <OUTPUT_DIR>
```
3. **Knowledge Graph Embedding**
The following command generates the embedding of the network passed to it.
```bash
$ clep embedding kge --data <NETWORK_FILE> --design <DESIGN_FILE> --model_config <MODEL_CONFIG.json> --train_size 0.8 --validation_size 0.1 --out <OUTPUT_DIR>
```
4. **Classification**
The following command carries out classification on the given data file for a chosen model (Elastic Net) using a chosen optimizer (Grid Search).
```bash
$ clep classify --data <EMBEDDING_FILE> --model elastic_net --optimizer grid_search --out <OUTPUT_DIR>
```
## Issues
If you have difficulties using CLEP, please open an issue at our [GitHub](https://github.com/hybrid-kg/clep) repository.
## Acknowledgements
### Citation
If you have found CLEP useful in your work, please consider citing:
[**CLEP: A Hybrid Data- and Knowledge- Driven Framework for Generating Patient Representations**](https://doi.org/10.1101/2020.08.20.259226
).<br />
Bharadhwaj, V. S., Ali, M., Birkenbihl, C., Mubeen, S., Lehmann, J., Hofmann-Apitius, M., Hoyt, C. T., & Domingo-Fernandez, D. (2020).<br />
*bioRxiv*, 2020.08.20.259226.
### Graphics
The CLEP logo and framework graphic was designed by Carina Steinborn.
## Disclaimer
CLEP is a scientific software that has been developed in an academic capacity, and thus comes with no warranty or guarantee of maintenance, support, or back-up of data.
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl clep-0.0.2:
pip install clep-0.0.2.whl
نصب پکیج tar.gz clep-0.0.2:
pip install clep-0.0.2.tar.gz