GDA develops and applies methods for analyzing large biological sets of data. We are leaders in analyzing time-series data, which proves invaluable for investigating dynamic changes in biological systems. We have worked on several different questions in biology: discovering patterns in biological data, detecting changes in behavior, inferring control relationships (causality), and developing mathematical models to uncover the mechanisms that drive biological systems.
Our data analysis of biological systems uses and extends methods from:
- Signal processing (ICA, Matching Pursuit)
- Network theory
- Machine learning
- Inference algorithms
- Information theory for neural networks
- Mathematical modeling (stochastic, deterministic, ODE/PDE, PBPK)
- Numerical simulation of mathematical models
Our domains of expertise include:
- Gene Regulatory Networks
- Chemical Reaction Networks
- Epidemiology & Health Data
- Biological membranes, fluids, and their interactions
- Computational pharmacology/toxicology
- Brain Computer Interfaces
Our software capabilities for biological data include customizable data analysis, simulation, and visualization. Most importantly, we perform extensive verification and validation of algorithms, which allows us to show how each algorithm performs on different types of biological data.
GDA projects related to biology have been funded by DHS and DARPA.