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: A method includes: receiving a query for a target health condition, a target compound from a research portal; identifying a set of edges coupling a target node representing the target health condition and a termination node representing the target compound in a semantic network, each edge in the set of edges includes intermediate nodes representing a set of patient characteristics and a composite action characteristic. The method also includes, in response to a first composite action characteristic falling within a first range and in response to a second composite action characteristic falling within a second range: isolating a first patient characteristic, contained in a first set of patient characteristics, as inclusion criteria; and defining a second patient characteristic, contained in a second set of patient characteristics, as exclusion criteria; and aggregating inclusion criteria and exclusion criteria into a specification for a clinical trial.

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: The presently disclosed subject matter relates to methods of determining a feline's susceptibility to developing chronic kidney disease (CKD) and to methods of preventing and/or reducing a risk of developing CKD for a feline. In certain embodiments, the biomarkers comprise creatinine, urine specific gravity or urea.

Abstract: The presently disclosed subject matter relates to methods of determining a feline's susceptibility to developing chronic kidney disease (CKD) and to methods of preventing and/or reducing a risk of developing CKD for a feline. In certain embodiments, the biomarkers comprise creatinine, urine specific gravity or urea.