guilt by association as a search principle
The exploitation of fundamental invariants is among the most elegant solutions to many computational problems in a wide variety of domains. One of the more powerful approaches to exploit invariants is the principle of "guilt by association". In particular, the principle of guilt by association is the foundation of remote homolog detection, protein function prediction, disease subtype diagnosis, treatment plan prognosis, and other challenges in computational biology. The principle suggests that two entities are in a specific relationship if they exhibit invariant properties underlying that relationship. For example, a protein is predicted to have a particular biological function if it exhibits the underlying invariant properties of that functional group---viz., guilty by association to other members of that functional group through the shared invariant properties. In my talk, I plan to present several facets of guilt by association in the computational prediction of protein function and draw parallels of these facets in information retrieval. Specifically, I plan to touch on the following facets: (a) the issue of chance associations; (b) novel generalizable forms of association; (c) fusion of multiple heterogeneous sources of evidence; (d) the dichotomy of knowing to a high degree of reliability that two entities are in some relationship and yet not knowing what that relationship is. I hope this talk will be, for the informational retrieval community, a window to the opportunities in computational biology that may benefit from the depth and variety of solutions information retrieval has to offer.
search principle