| 摘要 |
A method and system for interpreting experimental data with automated reasoning. Domain specific knowledge is acquired from one or more pharmaceutical information sources. A semantic representation of the domain specific knowledge is created that meets a desired set of criteria. A semantic representation includes applying relationships between words or symbols and their intended meanings and representing relationships among objects, ideas, or situations with a set of rules. Pharmaceutical data from a knowledge database is classified with the semantic representation. A set of reasons for any classified pharmaceutical data is provided. The set of reasons are used to help interpret the classified pharmaceutical data to remove errors such as "physical errors" (e.g., pipettor errors, common microphate preparation errors, microplate variances within runs, bio-chip errors, gel-electrophoresis errors, etc.) and "biological errors" such as errors in assays. Removing such errors helps improve fusion of knowledge from multiple data, information and knowledge sources (proprietary and public) which incorporates activity and selectivity against a target, desired pharmacokinetic and toxicity properties enabling selection of potential pharmaceutical compounds. The method and system may improve the identification, selection, validation and screening of new real or virtual pharmaceutical compounds. The method and system may also be used to provide new bioinformatic techniques for storing and manipulating pharmaceutical knowledge. |
