The overall focus is on using machine learning and Python tools to analyze biological data, estimate chemical bioactivity, and create reproducible workflows — a key aspect of modern bioinformatics projects.
ML models predicting biological activity based on chemical features
Notebooks explore data loading, feature generation, model training, and evaluation
Python app(s) (e.g., ML_app.py) which can serve as a backbone for interactive prediction tools
Pre-generated plots and figures illustrating results and data distributions
Example chemical bioactivity datasets
Supporting files such as pickled models (.pkl) and SMILES data files
Bioactivity_Prediciton_Notebooks/ ← Notebooks for data exploration & modeling
Mannwhitneyu_Test/ ← Statistical tests and comparisons
Plots/ ← Visual summaries (charts, figures)
bioactivity_corona_data/ ← Sample dataset (CSV + SMILES)
ML_app.py ← App script
bioactivity_prediction_app.ipynb ← Main prediction notebook
requirements.txt ← Python dependencies
logo.png ← Project logo
molecules.smi ← Example molecules data
- Clone the repo
git clone https://github.com/saamm/Bioinformatics.git
cd Bioinformatics
- Create a Python environment
python3 -m venv venv
source venv/bin/activate
- Install dependencies
pip install -r requirements.txt
- Launch notebooks or apps
Jupyter:
jupyter notebook
Python app (if configured with Streamlit or similar):
streamlit run ML_app.py
- For Educators / Learners
Explore notebooks to see workflows in action
Use example datasets to practice analysis
2. For Developers
Reuse model training functions
Integrate the app script into your own workflows
3. For Researchers
Adapt bioactivity prediction pipelines
Add your own datasets and compare outcomes
Made with passion for bioinformatics and open science