Vertical integration of high-throughput measurements to derive functional and regulatory interactions in S. cerevisiae

Bioinformatics concerns itself with developing data-driven tools for extracting biological information from high-throughput datasets (e.g. gene expression data, protein interaction data). Until recently, most of these tools focus on analyzing a single source of data. However, all high-throughput measurements are noisy and incomplete. Therefore, we can improve the accuracy of our data driven predictions by combining various high-throughput measurements. In this thesis, we take a couple of steps towards data integration, which are reported in this thesis. We compare and introduce several computational methods for integrating different measurements