Anthony Gitter




(608) 316-4442


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My group develops computational approaches to reconstruct signaling pathways and transcriptional regulatory networks from multiple types of high-throughput data using techniques from machine learning and graph theory. We emphasize dynamic networks and disease applications in virology and oncology.


Ph.D., 2012, Carnegie Mellon University

Selected Publications

  • Tuncbag N., Gosline S.J., Kedaigle A., Soltis A.R., Gitter A., Fraenkel E. (2016). Network-based interpretation of diverse high-throughput datasets through the Omics Integrator software package. PLoS Computational Biology, 12(4), e1004879.
  • Gitter A., Braunstein A., Pagnani A., Baldassi C., Borgs C., Chayes J., Zecchina R., & Fraenkel E. (2014). Sharing information to reconstruct patient-specific pathways in heterogeneous diseases. Pacific Symposium on Biocomputing, 39-50.
  • Gitter A. & Bar-Joseph Z. (2013). Identifying proteins controlling key disease signaling pathways. Bioinformatics, 29(13), i227-i236.
  • Gitter A., Carmi M., Barkai N., & Bar-Joseph Z. (2013). Linking the signaling cascades and dynamic regulatory networks controlling stress responses. Genome Research, 23(2), 365-376.
  • Bar-Joseph Z., Gitter A., & Simon I. (2012). Studying and modelling dynamic biological processes using time-series gene expression data. Nature Reviews Genetics, 13(8), 552-564.