SUMMARY
Daniel P. Wickland, Ph.D., develops computational methods to analyze high-dimensional genomic data. Dr. Wickland applies those approaches to identify biomarkers associated with disease and treatment response.
As a bioinformatician, Dr. Wickland collaborates closely with laboratory scientists and clinician-investigators to provide analytic support for translational research studies in multiple complex diseases. This includes glioma, breast cancer and Alzheimer's disease. His work aids the design of novel personalized therapeutics to empower precision medicine.
Focus areas
- Cancer immunotherapy. Bioinformatic prediction of tumor neoantigens for use in personalized cancer vaccines that leverage the immune system to combat cancer.
- RNA therapeutics. Development of a machine-learning-based computational platform to design RNA-targeted therapeutic molecules for cancer and neurological disease.
- Pharmacogenomics. Investigation of tumor biomarkers that predict responses to drug therapies.
- Alzheimer's disease. Identification of genetic and transcriptional factors that influence susceptibility to young-onset Alzheimer's disease and contribute to its clinical heterogeneity.
Significance to patient care
Research studies lead to large amounts of genomic data. These data have huge potential to affect patient health. Dr. Wickland uses advanced computational approaches to get meaning from these data. He uses that information to support precision medicine.
Dr. Wickland's work helps researchers learn which genetic factors make people more likely to get a disease or respond better to treatment. This information can lead to earlier diagnoses and guide treatment choices. New analytic methods to design new biotherapeutics can support the development of new treatments tailored to the needs of individual patients.