William Bechtel (University of California, San Diego)
Machine-learning tools, including tools for analyzing network representations of big data in cancer research, do not seem to characterize mechanisms. By annotating the results with information from Gene Ontology (GO), researchers attempt to interpret networks in terms of knowledge about cell components and operations. Recently researchers have used machine-learning techniques to create an alternative ontology, NeXO, directly from data. Although on their surface, neither GO nor NeXO describe mechanisms, the hierarchical structured resulting from employing directed acyclic graphs provides the needed bridge. I will discuss how mechanistic knowledge embodied in these ontologies is informing network analyses of cancer data.
