AI & ML interests
Our center develops and applies a variety of computational biology and data science-based methods, tools, and algorithms to generate knowledge that can advance human health and disease research.
Our mission
We are a multi-disciplinary computational team of computational biologists, bioinformaticians, computer scientists, and software engineers. Each member of the lab is trained to use these approaches to address research questions relating to disease biology and precision medicine. We collaborate with clinicians, researchers, and foundations in the US and worldwide to translate data into knowledge to drive the discovery, development, and delivery of omics-based precision medicine. As part of the UAB School of Medicine and Department of Genetics, we aspire to help patients, families, and those who take care of them by developing and applying groundbreaking tools and strategies for diagnosis and prognosis. We train individuals with a biology background who would like to develop computational skills. We also train individuals with a computer science background who would like to develop biological understanding so that they can study disease and improve patient outcomes. If you want to join our team, please reach out.
What do we do? What is our science? How do we do it?
Our center develops and applies a variety of computational biology and data science-based methods, tools, and algorithms to generate knowledge that can advance human health and disease research.
We develop:
- Tools and methods for molecular diagnosis and prognosis
- Strategies for the discovery of genotype-phenotype associations, including identification of clinically relevant modifiers
- Commercial-grade tertiary analysis software and other tools to enable scientific discovery and data interpretation
To aid in:
- Uncovering the cause of disease
- Understanding differences in disease presentation
- Identification of potential treatment and processes
By applying:
- Computational biology approaches such as systems biology, multi-omics, comparative genomics, structural biology, biomodeling, ontologies
- Machine learning methods, including data exploration, feature selection, modeling, explainable AI, interpretability, visualization
- Software development expertise for design, architecture, algorithm development, usability