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pharmacovigilance-literature-mining

Co-occurrence analysis in pubmed and faers of two lists of terms.

https://github.com/bmascat/pharmacovigilance-literature-mining

Science Score: 67.0%

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  • ✓
    CITATION.cff file
    Found CITATION.cff file
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    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 3 DOI reference(s) in README
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    Academic publication links
    Links to: zenodo.org
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    Scientific vocabulary similarity
    Low similarity (15.4%) to scientific vocabulary

Keywords

adverse-drug-reaction drugs faers matplotlib-pyplot pandas pubmed python streamlit
Last synced: 6 months ago · JSON representation ·

Repository

Co-occurrence analysis in pubmed and faers of two lists of terms.

Basic Info
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  • Stars: 11
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Topics
adverse-drug-reaction drugs faers matplotlib-pyplot pandas pubmed python streamlit
Created about 2 years ago · Last pushed 12 months ago
Metadata Files
Readme License Citation

readme.md

DOI

PubMed Co-occurrence and FAERS Analysis

This application performs a co-occurrence analysis on PubMed of two lists of terms. The use case of the application would be diseases (list 1) and drugs (list 2). The app also extracts the adverse effects from the list of drugs and allows an optional third list to filter out the adverse effects of interest.

Features

  • Co-occurrence Analysis: The application uses PubMed data to perform a co-occurrence analysis of disease and drug terms.

Co-occurrence Analysis

  • Adverse Effects Extraction: In addition to the co-occurrence analysis, the application extracts adverse effects from the provided list of drugs.

    Adverse Effects Extraction

  • Adverse Effects Filtering: An optional third list can be used to filter adverse effects of interest, enabling a more specific and personalized analysis.

    Adverse Effects Filtering

Usage

  1. Data Loading: Load the CSV files containing the lists of diseases and drugs. You can also load an additional file for the list of adverse effects, if necessary.

  2. Analysis: Once the files are loaded, click on the "Analyze" button to start the analysis. The application will process the data and display the results of co-occurrence and adverse effects.

  3. Results Visualization: The analysis results will be presented in the form of charts and tables, making them easy to understand and analyze.

  4. Data Download: You can download the analysis results for further analysis or reference.

Running Locally

To run the application locally, follow these steps:

  1. Create a directory named .streamlit in the root directory of the project.
  2. Create a file named secrets.toml inside the .streamlit directory.
  3. Add your FAERS API key to the secrets.toml file with the following format: toml FAERS_API_KEY = "your_api_key_here" You can obtain your FAERS API key here.

For more information on how secrets work in Streamlit, visit this link.

Built With

  • Python
  • Streamlit framework

Contribution

If you find any bugs, have ideas for new features or improvements, or simply want to contribute in any way to the development of this application, feel free to open an issue or send a pull request on GitHub!

License

This project is licensed under the AGPLv3 License.

Citation

If you use this repository in your work, please cite it as follows:

Bea-Mascato, B. (2024). BreisOne/literature-mining: Literature-mining app v1.0.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10863935

Owner

  • Name: Brais
  • Login: bmascat
  • Kind: user

Data science, bioinformatics and software development

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Bea-Mascato
  given-names: Brais
  orcid: https://orcid.org/0000-0003-1588-2897
date-released: '2024-03-23'
doi: 10.5281/zenodo.10863935
license:
- cc-by-4.0
repository-code: https://github.com/BreisOne/literature-mining/tree/v1.0.0
title: 'BreisOne/literature-mining: Literature-mining app v1.0.0'
type: software
version: v1.0.0

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Dependencies

app/literature_mining/setup.py pypi
app/requirements.txt pypi
  • matplotlib >=3.8.3
  • pandas >=2.1.1
  • pymed >=0.8.9
  • requests >=2.31.0
  • seaborn >=0.13.2