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📅 Day 1: Foundational Data Setup
- Focus: Building the core math foundations and setting up biological datasets.
- Learning how to use conjugate priors and likelihood estimation to predict disease risks and variant changes.
- Using SVD and eigenvalues to reduce high-dimensional biological data into simple, visual groups.
- Writing custom, fast mathematical code using hardware acceleration to understand how AI models learn across a 3D loss surface.
- Accessing, downloading, and cleaning raw genomic and protein data from online databases such as NCBI and EBI.
🛠️ Hands-on:
- Write a Python script using PyMC to calculate disease risk probabilities with clear uncertainty boundaries.
- Build a basic SVD matrix system from scratch using NumPy to sort and group complex genetic data.
🧰 Tools Covered: Python, PyMC, NumPy, NCBI, EBI, Google Colab
📅 Day 2: Data Engineering & Large-Scale Workflows
- Focus: Managing massive datasets and linking different coding tools without crashing your computer’s memory.
- Using Apache Arrow to read, filter, and clean huge data files without overloading your computer’s RAM.
- Setting up a workflow where Python handles the machine learning code and R handles the deep statistical analysis in the same environment.
- Handling heavy genomic datasets by splitting the work across distributed computing networks.
- Designing workflow paths to match candidate drugs with specific biological targets.
🛠️ Hands-on:
- Use Polars and Dask to run sorting and aggregation filters over a large-scale public health dataset.
- Run speed and memory benchmarks to find the fastest way to join two large biological data tables.
🧰 Tools Covered: Python, R, Apache Arrow, Polars, Dask, Google Colab
📅 Day 3: Sequence Models, Automation & Applications
- Focus: Fine-tuning transformer models, handling rare data, and deploying visual AI applications.
- Transformers for Biology: Understanding how deep learning architectures read DNA and protein sequences like sentences, including tools such as AlphaFold.
- Model Fine-Tuning: Customizing pre-trained language and sequence models on small, specific biological datasets using PyTorch Lightning.
- Managing Unbalanced Data: Using specialized loss functions such as Focal Loss to train AI models for rare diseases and anomaly detection.
- Live Dashboards: Creating interactive web apps that display model predictions, data trends, and feature importance in real time.
- Ethics & Future Trends: Discussing AI bias in health metrics, data privacy, and how autonomous laboratories generate new scientific hypotheses.
🛠️ Hands-on:
- Deploy an automated AI pipeline designed to flag rare anomalies in highly imbalanced medical datasets.
- Build a live-updating web app using Plotly Dash that displays automated predictions and structural metrics instantly.
🧰 Tools Covered: Python, PyTorch Lightning, Transformers, Focal Loss, Plotly Dash, AlphaFold, Google Colab
