Research Areas:
- Biostatistics
- Missing Data
- Generalizing Trial Findings to Target Populations
- Incorporating Mobile and Digital Health Data Into Clinical Trials
- Design of Pragmatic Clinical Trials
The Ashley lab is focused on the application of whole genome sequencing to the medical care of individuals and families. We lead the Stanford Center for Inherited Cardiovascular Disease, one of the few medical centers in the country where patient genome sequences can be readily incorporated into clinical care. In 2010, we led the team of BMI faculty that completed the first clinical interpretation of a human genome. We extended this to a pipeline that would handle families in 2011. We are also fascinated by network biology. Part of the Stanford heart transplant team, we are focused on understanding the heart’s response to disease or exercise stress. We are part of a team of three major transplant centers that was recently awarded $9m to explore the genetic control of cardiac transcriptional activity via RNA sequencing and network modeling. Finally, although many of our questions can be answered in silico, to establish causality, we turn to the wet lab to explore the biology of key genes and signaling modules.
Research Areas:
- Genetics & Genomics
- Multi-Omics
- Machine Learning
- Deep Learning
- Digital Health
- Human Performance
- Network Biology
Research Areas:
- Biomedical Informatics
- Artificial Intelligence (AI)
- Machine Learning
- Pharmacogenomics
- Structural Bioinformatics
- Natural Language Processing
- Knowledge Graphs
- Drug Discovery
- Pharmacology
We develop a wide range of machine learning algorithms and are especially interested in extracting disease insights from population genomics and epigenomics. On the methodology side, we are investigating new approaches to adaptive data analysis, representation learning for bio-medical data, new probabilistic models that encourage diversity, and multi-view learning. Application topics include: whole-genome and exome sequence analysis, risk prediction, synthetic biology, chromatin dynamics and transcription regulation.
Research Areas:
- Machine/Statistical Learning
- AI for Health
- Computational Biology