A student-friendly paper and guide to applying ML to biological data. Covers core techniques and examples.
Official Google intro for beginners to understand Colab notebooks and run code with zero setup.
A high school-level introduction to how biology and computer science work together to solve problems.
A simple PDF explaining how to commit, push, and organize a GitHub repo for your project. Perfect for challenge submissions.
Tackle real-world problems in biology and AI through student-led competitions like the NeuroBio Challenge.
A modern biology lab notebook platform, great for managing DNA/protein sequences, experimental data, and protocols.
The industry standard for version control, collaboration, and sharing code. All participants are required to submit via GitHub.
A large collection of ready-to-use machine learning datasets, including biomedical and genomics data.
A rich database of gene expression datasets across diseases, tissues, and experiments. Ideal for ML classification or correlation projects.
Explore real-world cancer mutation and expression data. Useful for AI models that predict disease from genomic patterns.
A clean README template that students can fork and fill in with project details, methods, and dataset references.
A starter notebook with project sections: setup, dataset import, preprocessing, model building, and analysis. (Link Coming Soon)