4. Biological Network Analysis

Biological network data contains essential information about potential regulators and functional modules in diverse biological processes. My analyses of CRISPR screens suggest that protein interaction networks, when integrated with gene expression or histone marks, are highly predictive of regulator genes in cancer cell vulnerability (Jiang et al., Genome Biol 2015). Meanwhile, the quality of CRISPR screen data can be significantly enhanced through network information using a computational method NEST (Network Essentiality Scoring Tool) developed by myself. I also developed a fast network clustering algorithm SPICi (Speed and Performance In Clustering) to identify functional gene modules (Jiang et al., Bioinformatics 2010). SPICi has a time complexity O(V log V+E) and space complexity O(E), where V and E are the number of vertices and edges in the network.

Network Essentiality Scoring Tool