Daniel Danis, Ph.D.
Associate Computational Scientist
I work on development of new algorithms designed to analyze big data to enable precision medicine
I studied Pharmacy and Normal and Pathological Physiology at Comenius University, Bratislava, Slovakia. During my PhD. study, I developed bioinformatics pipelines for analysis of Whole exome sequencing data with aim to identify DNA mutations leading to metabolic or sensory disorders.
At my current position, I work on development of novel algorithms and software tools for integrative analysis of clinical/phenotype data with high-throughput genomics data, such as long read whole genome sequencing.
This work includes ingest, curation, and management of large volumes of data which I use for training and validation of statistical models. I am also responsible for assembly of custom bioinformatics pipelines and their deployment on distributed computational systems. Finally, I work on evaluation of models on real patient data with aim to improve diagnostics and enable precision medicine.
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Research HighlightJuly 20, 2021Finding splice variants of clinical importance with SQUIRLS
Professor Peter Robinson, M.D., MSc., and Associate Computational Scientist Daniel Danis, Ph.D., used machine learning and datasets of splice variants associated with Mendelian (single-gene, inherited) disorders to develop a new algorithm to develop a new algorithm to improve clinical diagnostics.