| 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 meeting a desired set of criteria. Pharmaceutical data from a knowledge database is classified with the semantic representation, allowing construction of a set of reasons for any classified pharmaceutical data. The set of reasons may help interpret the classified pharmaceutical data to remove errors, such as "physical errors" and "biological errors". Removing such errors helps improve fusion of knowledge from multiple data, information and knowledge sources 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 identification, selection, validation and screening of new real or virtual pharmaceutical compounds or may be used to provide bioinformatic techniques for storing and manipulating pharmaceutical knowledge. |
