Dr. Bonneau focuses on two main categories of computational biology: learning networks from functional genomics data and predicting and designing protein and peptoid structure. In both areas he has played key roles in achieving critical field-wide milestones. In the area of structure prediction he was one of the early authors on the Rosetta code, which was one of the first codes to demonstrate accurate and comprehensive ability to predict protein structure in the absence of sequence homology.  His lab has also made key contributions to the areas of genomics data analysis. They focus on two main areas, both relevant to this project: 1) methods for network inference that learn dynamics and topology from data (the Inferelator) , and 2) methods that learn condition dependent co-regulated groups from integrations of different genomics data-types (cMonkey integrative biclustering).