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BioLitAI: Intelligent Literature Mining and Research Gap Detection Tool

An AI-powered web application for biomedical researchers to automatically search, summarize, and analyze scientific literature to identify research gaps.

Features

  • 🔍 Literature Search: Search PubMed for biomedical papers
  • 📝 Intelligent Summarization: Auto-summarize papers using Mistral-7B LLM
  • 🏷️ Named Entity Recognition: Extract diseases, genes, drugs, methods, and datasets
  • 💡 Research Gap Detection: AI-powered analysis to suggest potential research gaps
  • 💾 SQLite Database: Store and retrieve research findings
  • 🎨 Interactive UI: Streamlit-based user interface

Tech Stack

  • Backend: Python
  • UI Framework: Streamlit
  • NLP Models: Hugging Face Transformers (Mistral-7B), spaCy
  • Database: SQLite
  • API: PubMed (free public endpoint)

Project Structure

BioLitAI/
├── README.md
├── requirements.txt
├── config.py
├── src/
│   ├── __init__.py
│   ├── pubmed_api.py          # PubMed API wrapper
│   ├── llm_summarizer.py      # LLM-based summarization
│   ├── ner_extractor.py       # Named Entity Recognition
│   ├── gap_detector.py        # Research gap detection
│   └── database.py            # SQLite database operations
├── app.py                      # Streamlit main application
└── data/
    └── bioliteai.db           # SQLite database (auto-created)

Installation

  1. Clone the repository

    git clone https://github.com/sufaismail/BioLitAI.git
cd BioLitAI

  2. Create a virtual environment

    python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

  3. Install dependencies

    pip install -r requirements.txt

  4. Download spaCy model

    python -m spacy download en_core_sci_md

Usage

Run the Streamlit app:

streamlit run app.py

Then open your browser to http://localhost:8501

How It Works

  1. Search: Enter keywords to search PubMed
  2. Analyze: Papers are automatically summarized and analyzed
  3. Extract: Diseases, genes, drugs, methods, datasets are extracted
  4. Detect Gaps: AI identifies potential research gaps
  5. Store: Results are saved to the database for future reference

Features in Development

  • Export results to PDF/CSV
  • Advanced filtering and sorting
  • Collaboration features
  • Citation management
  • Multi-language support

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License

Author

sufa ismail

Support

For issues, questions, or suggestions, please open an issue on GitHub.