deductive biocomputing演绎生物运算.pdfVIP

Deductive Biocomputing 1 2 2 3 Jeff Shrager *, Richard Waldinger , Mark Stickel , J. P. Massar 1 Department of Plant Biology, Carnegie Institution of Washington, Stanford, California, United States of America, 2 Artificial Intelligence Center, SRI International, Menlo Park, California, United States of America, 3 Berkeley, California, United States of America Background. As biologists increasingly rely upon computational tools, it is imperative that they be able to appropriately apply these tools and clearly understand the methods the tools employ. Such tools must have access to all the relevant data and knowledge and, in some sense, ‘‘understand’’ biology so that they can serve biologists’ goals appropriately and ‘‘explain’’ in biological terms how results are computed. Methodology/Principal Findings. We describe a deduction-based approach to biocomputation that semiautomatically combines knowledge, software, and data to satisfy goals expressed in a high-level biological language. The approach is implemented in an open source web-based biocomputing platform called BioDeducta, which combines SRI’s SNARK theorem prover with the BioBike interactive integrated knowledge base. The biologist/user expresses a high-level conjecture, representing a biocomputational goal query, without indicating how this goal is to be achieved. A subject domain theory, represented in SNARK’s logical language, transforms the terms in the conjecture into capabilities of the available resources and the background knowledge necessary to link them together. If the subject domain theory enables SNARK to prove the conjecture—that is, to find paths between the goal and BioBike resources—then the resulting proofs represent solutions to the conjecture/query. Such proofs provide provenance for each result, indicating in deta