Dr. Anthony Gitter is an Assistant Professor of Biostatistics and Medical Informatics at the University of Wisconsin-Madison and an Investigator at the Morgridge Institute for Research. His computationally-focused lab develops network algorithms to model transcriptomic and proteomic data. They apply these methods to study cellular stress responses, viral infection, and viral-induced cancers. In addition, his lab creates machine learning approaches to determine how to prioritize biological experiments, especially chemical screening for drug discovery.
Dr. Gitter received his B.S. in Computer Science from Arizona State University. His first research experience with Dr. Chitta Baral and Dr. Graciela Gonzalez combined text mining and crowdsourcing to extract protein-protein and gene-disease relationships from biomedical abstracts. During his Ph.D. in Computer Science at Carnegie Mellon University with Dr. Ziv Bar-Joseph, he designed computational methods to interpret changes in gene expression and protein activity through biological networks. His postdoctoral position was joint between Dr. Ernest Fraenkel’s lab at MIT and Microsoft Research New England, directed by Dr. Jennifer Chayes. As a postdoc, Dr. Gitter developed new algorithms to detect different genetic mutations in cancer that have unexpectedly similar consequences. He applied these methods to study pediatric cancer with collaborators at Boston Children’s Hospital and the Broad Institute.
In 2014, Dr. Gitter started his independent lab at the University of Wisconsin-Madison and the Morgridge Institute for Research. As a member of the Rowe Center for Research in Virology at the Morgridge Institute, he enjoys having his computational lab embedded among those of his wet lab collaborators and values using computational predictions to influence experimental design. Dr. Gitter received an NSF CAREER award in 2016 to develop algorithms that infer network models from signaling and transcriptional data collected over time by tracking which cellular events happen before others. For instance, analyzing the timing of phosphorylation changes during cellular stimulus response can predict the direct targets of kinases and phosphatases, as demonstrated in his lab’s recent publication. Similar time series modeling ideas underlie their preprint about predicting transcriptional regulators from pseudotime-annotated single-cell RNA-sequencing data.
Dr. Gitter also recently returned to his roots in crowdsourcing science by joining Dr. Casey Greene in a collaborative review about deep learning in biology and medicine. In a novel form of scientific writing, the open project was written on GitHub and attracted over 40 contributors, including other members of the New PI Slack community. Dr. Gitter has teamed with Dr. Greene and Dr. Daniel Himmelstein to expand this writing approach into the Manubot platform. Ongoing development will make Manubot manuscripts more interactive and more accessible to a non-technical audience.