| Networks describe the interactions between different objects. In living systems, knowing which biological objects interact with each other would deepen our understanding of the functions of both individual objects and their working modules. Due to experimental limitations, currently only small portions of these interaction networks are known. This thesis describes methods for computationally inferring the complete networks based on the known portions and related data. These methods exploit special data properties and problem structures to achieve high accuracy. The training set expansion method handles sparse and uneven training data by learning from information-rich regions of the network, and propagating the information to help learn from the information-poor regions. The multilevel learning framework combines information at different levels of a concept hierarchy, and lets the predictors at the different levels to propagate information between each other. Combined optimization between levels allow the integrated use of data features at different levels to improve prediction accuracy and noise immunity. Finally, proper incorporation of heterogeneous data facilitates the identification of interactions uniquely detectable by each kind of data. This thesis also describes some work on data integration and tool sharing, which are crucial components of network analysis studies. |
