Published January 30, 2025 | Version 0.1
Software Open

PubMed ChemInsight - Unlock Insights from PubMed

  • 1. ROR icon Edelweiss Connect (Switzerland)

Description

PubMed ChemInsight is an intuitive web-based tool designed to help researchers efficiently retrieve and analyze scientific literature related to specific compounds and their interactions. By leveraging PubMed and PubChem, the tool allows users to input multiple compounds, genes, and custom keywords to refine searches and extract relevant studies quickly.

🔹 Key Features:

  • Compound & Target-Based Search: Retrieve PubMed articles by specifying compounds and their interaction targets with an option to add additional keywords.
  • Automated Synonym Matching: Expands searches using common synonyms from PubChem.
  • Advanced Filtering: Search within a specified date range and remove duplicates.
  • Exportable Results: Download search results as a CSV file for further analysis.

🔹 How to Access & Use:

  • Access the tool: PubMed ChemInsight
  • Steps to use:
    1️⃣ Enter compounds and optional genes or keywords.
    2️⃣ Set filters such as synonyms and date range.
    3️⃣ Click "🚀 Launch Search" to retrieve articles.
    4️⃣ Review results in a tabular format and export if needed.

This tool simplifies literature searches, helping researchers quickly gather insights from PubMed for informed decision-making. 🚀

Files

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Additional details

References

  • National Center for Biotechnology Information (NCBI). "Entrez Programming Utilities (E-utilities)." Available at: https://www.ncbi.nlm.nih.gov/books/NBK25501/
  • Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., Wang, J., Yu, B., Zhang, J., & Bryant, S. H. (2016). "PubChem Substance and Compound databases." Nucleic Acids Research, 44(D1), D1202–D1213. DOI: 10.1093/nar/gkv951
  • Metapub Python Library for PubMed data retrieval. Available at: https://pypi.org/project/metapub/
  • Treuille, A., Nate, P., & Zhenyao, Z. "Streamlit: An open-source app framework for Machine Learning and Data Science projects." Available at: https://docs.streamlit.io/
  • McKinney, W. (2010). "Data Structures for Statistical Computing in Python." Proceedings of the 9th Python in Science Conference, 51–56. DOI: 10.25080/Majora-92bf1922-00a
  • Reitz, K., & Bader, T. (2015). "Requests: HTTP for Humans." Available at: https://docs.python-requests.org/en/latest/