AI & Computational Methods
Developing advanced AI and machine learning methods to solve complex genomics and systems biology problems.
AI & Computational Methods
Our lab develops cutting-edge AI and machine learning methods specifically designed to solve challenging problems in genomics and systems biology. We create innovative algorithms that can extract meaningful biological insights from complex, high-dimensional genomic data, enabling discoveries that would be impossible through traditional approaches.
Key Research Areas
Machine Learning & Deep Learning
- Graph Neural Networks: Developing specialized architectures for modeling molecular interactions and biological networks
- Transfer Learning: Adapting pre-trained models for biomedical applications with limited labeled data
- Representation Learning: Creating informative embeddings for genes, variants, and biological entities
- Interpretable AI: Building transparent models that provide biological insights alongside predictions
Statistical Methods
- Graphical Models: Employing Bayesian networks and probabilistic graphical models for modeling regulatory relationships
- High-Dimensional Statistics: Developing methods for analyzing genomic data with thousands of variables
- Causal Inference: Inferring causal relationships from observational biological data
- Regularization Methods: Implementing sparse learning techniques (Lasso, elastic net) for feature selection in genomic studies
Data Integration
- Multi-Omics Integration: Combining genomics, transcriptomics, proteomics, and other data types
- Knowledge Graph Construction: Building comprehensive biological knowledge bases
- Cross-Species Analysis: Leveraging model organism data to understand human biology
- Heterogeneous Data Fusion: Integrating structured databases with unstructured literature
Methodological Innovations
Signal Pathway Activity Prediction
Quantitative approaches for inferring signaling pathway activity from genome-wide expression data, enabling systems-level understanding of cellular responses.
Network-Based Methods
Leveraging biological network topology to prioritize disease genes, predict gene function, and identify therapeutic targets.
Dimensionality Reduction
Advanced techniques for visualizing and analyzing high-dimensional single-cell data while preserving biological structure.
Open-Source Tools & Software
We are committed to making our methods accessible to the broader research community through well-documented, user-friendly software packages. Our tools are designed with both computational biologists and wet-lab researchers in mind.
Collaborative Research
Our computational methods are developed in close collaboration with experimental biologists and clinicians, ensuring that our approaches address real-world biological questions and can be validated experimentally.