Developing statistical methods for analysis of complex traits
Advancing mass spectrometer technology by developing instrumentation, chemistry, & informatics
Creating machine-learning methods for inferring networks of interacting genes, proteins, environmental variables, and phenotypes
Developing statistical and algorithmic approaches for the analysis of biological sequence data
Elucidating systems-level relationships between genotype-phenotype-environment in eukaryotic stress responses
The Hittinger Lab uses yeast carbon metabolism as a model for basic bioenergy, biomedical, and evolutionary research.
Developing statistical genomic methods, especially with regard to noncoding regulatory sequences and effects of their variation
Innovating statistical methods for the analysis of high-throughput genomics data, including bulk and single-cell transcriptomic measurements
Developing statistics and empirical Bayes methods for contemporary biomedical applications
A bioengineering approach to understanding fundamental biological and medical questions in microbes, specifically model and pathogenic yeasts.
Elucidating bacterial genome evolution among plant and animal pathogens
Systems and synthetic biology approach to understanding and designing biology
Realizing fully integrated single-molecule systems for the understanding and fabrication of genomes
Innovating the development of powerful new technologies to drive biological research
Innovating massively parallel genomic technologies and applying to understand how the plasma membrane functions in all cells
Characterizing sex-differential risk mechanisms in autism and other neuropsychiatric disorders using functional genomics, human genetics and
Center for Genomic Science Innovation
University of Wisconsin-Madison
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Madison, WI 53706
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