Computational Methods

Our lab develops and applies various computational methods to address complex problems in biology. This page outlines some of the key methods we use in our research.

Statistical Analysis

We employ a range of statistical techniques to analyze biological data, including: - Bayesian inference - Multivariate analysis - Time series analysis - Spatial statistics

Machine Learning Approaches

We utilize and develop machine learning methods including: - Deep learning for biological sequence analysis - Reinforcement learning for optimizing experimental design - Transfer learning for cross-species prediction - Interpretable AI for biological insights

Mathematical Modeling

Our mathematical modeling approaches include: - Ordinary and partial differential equations - Stochastic processes - Agent-based models - Network models

Software & Tools

We develop open-source software tools to facilitate biological research: - [Tool 1]: Brief description - [Tool 2]: Brief description - [Tool 3]: Brief description

Data Science Pipeline

Our typical data analysis pipeline includes: 1. Data collection and quality control 2. Preprocessing and normalization 3. Feature selection and dimensionality reduction 4. Model development and validation 5. Biological interpretation and experimental validation

This page will be expanded with specific methodologies and tools as our research progresses.