Abstract: A method for automated therapy discovery includes: accessing a corpus of scientific publications; compiling a population of semantic concepts from the corpus of scientific publications into a vector space model; deriving domains of concepts in the vector space model based on proximity to domain descriptors in the vector space model; deriving association scores and action characteristics between connected concepts, based on proximity and action descriptors in the vector space model; generating a semantic network; receiving a query for a target concept and a target domain at a research portal; isolating a set of edges between a target node and a subset of nodes; identifying subsets of concepts along the set of edges; generating hypotheses for directions and magnitudes of effects of subsets of concepts on the target concept based on association scores and action characteristics stored in connections along the set of edges; and returning hypotheses to the research portal.

Abstract: One variation of a method includes: accessing a corpus of scientific publications; compiling a population of semantic concepts from the corpus of scientific publications into a vector space model; deriving domains of concepts in the vector space model; deriving association scores and action characteristics between connected concepts in the vector space model; and generating a semantic network. This variation of the method further includes: receiving a query for a target concept and a target domain at a research portal; isolating a set of edges between a target node and a subset of nodes in the semantic network; identifying concepts along the set of edges in the semantic network; generating hypotheses for directions and magnitudes of effects of concepts on the target concept based on association scores and action characteristics stored in connections along the set of edges; and returning hypotheses to the research portal for a user to review.