Recent developments in biotechnology have enabled quantitative measurement of diverse cellular phenomena. For instance, microarray technology allows biologists to measure the expression of all genes in the genome on a single chip. Other technology allows high-throughput measurement of physical interactions between proteins, which are an important mechanism behind most cellular processes. These recent developments have generated an unprecedented amount of data for several different organisms. These data promise to revolutionize our understanding of biology, but integrating information across several noisy, heterogeneous datasets to derive holistic models of the cell requires sophisticated computational approaches.
Our research focuses on machine learning approaches for integrating diverse genomic data to make inferences about biological networks. The main purpose of our work is to further our understanding of gene function and how genes or proteins interact to carry out cellular processes.
Benjamin VanderSluis
Post-Doc
Kevin Lin
Ph.D. Student
Hamid Safizadeh
Ph.D. Student
Henry Ward
Ph.D. Student
Xiaotong Liu
Ph.D. Student
AHM Mahfuzur Rahman
Ph.D. Student
Maximilian Billmann
Post-Doc
Justin Nelson
Ph.D. Student
Jean-Michel Michno
Ph.D. Student
Scott Simpkins
Ph.D. Student
Elizabeth Koch
Ph.D. Student
Rob Schaefer
Ph.D. Student
Trina Kruiger-Laber
Master's Student
Carles Pons
Post-Doc
Yungil Kim
Post-Doc
Raamesh Deshpande
Ph.D. Student
Stephanie DiPrima
Master's Student
Roman Briskine
Ph.D. Student
Jeremy Bellay
Post-Doc
Colin Pesyna
Member
Avantika Chaudhary
Member
Tahin Syed
Member
Joshua Baller
Ph.D. Student
Joe Jeffers
Undergraduate Researcher
Bill Busch
Researcher
Cici Xu
Undergraduate Researcher
Michael Albrecht
Undergraduate Researcher
Eric Schultz
Undergraduate Researcher
Nathaniel Budijono
Undergraduate Researcher