Metadata-Version: 2.1
Name: clep
Version: 0.0.1
Summary: A Hybrid Data and Knowledge Driven Framework for Generating Patient Representations
Home-page: https://github.com/hybrid-kg/clep
Author: Vinay Bharadhwaj
Author-email: vinay.srinivas.bharadhwaj@scai.fraunhofer.de
Maintainer: Vinay Bharadhwaj
Maintainer-email: vinay.srinivas.bharadhwaj@scai.fraunhofer.de
License: See LICENSE file
Project-URL: Bug Tracker, https://github.com/hybrid-kg/clep/issues
Project-URL: Source Code, https://github.com/hybrid-kg/clep
Project-URL: Documentation, https://clep.readthedocs.io/en/latest/
Description: <p align="center">
          <img src="docs/source/logo.jpg">
        </p>
        
        <h1 align="center">
          CLEP: A Hybrid Framework for Generating Patient Representations
          <br/>
          <img src="https://travis-ci.com/hybrid-kg/clep.svg?branch=master" />
          <img src='https://readthedocs.org/projects/clep/badge/?version=latest' alt='Documentation Status' />
        </h1>
        
        ## Table of contents
        * [General info](#general-info)
        * [Installation](#installation)
        * [Documentation](#documentation)
        * [Input data](#input-data-formats)
        * [Usage](#usage)
        * [Issues](#issues)
        * [Acknowledgements](#acknowledgements)
        * [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 most recent code can be installed from the source on [GitHub](https://github.com/hybrid-kg/clep) with:
        
        ```
        $ python3 -m 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:
        
        ```
        $ git clone https://github.com/hybrid-kg/clep.git
        $ cd clep
        $ python3 -m 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.
        
        ```
        $ python3 -m 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).
        
        ```
        $ python3 -m 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.
        
        ```
        $ python3 -m 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).
        
        ```
        $ python3 -m 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
        ### 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.
        
Keywords: Bioinformatics,Machine Learning,Prediction Models,Embeddings
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: docs
