Method and system for interpreting and validating experimental data with automated reasoning

A method for interpreting experimental data with automated reasoning, comprising:

• acquiring domain specific knowledge from a plurality of pharmaceutical information sources;
• creating a semantic representation of the domain specific knowledge that meets a desired set of criteria;
• classifying pharmaceutical data from a knowledge database with the semantic representation;
• providing a set of reasons for any classified pharmaceutical data, wherein the set of reasons are used to help interpret the classified pharmaceutical data;
• determining with the set of reasons whether any classified pharmaceutical data includes data related to physical errors or biological errors, and if so,
• marking classified pharmaceutical data related to physical errors or biological errors
• as unreliable in the knowledge database, thereby validating any fused knowledge
• created from the knowledge database.

A method for interpreting experimental data with automated reasoning, comprising:

• acquiring domain specific knowledge from a plurality of pharmaceutical information sources;
• creating a semantic representation of the domain specific knowledge that meets a desired set of criteria, wherein the semantic representation includes plurality of rules to identify physical errors or biological errors in a plurality of screening processes used to collect pharmaceutical data;
• classifying a plurality of errors patterns in pharmaceutical data from a knowledge database with the semantic representation;
• providing a set of reasons for any classified pharmaceutical data, wherein the set of reasons are used to annotate error patterns to help interpret physical errors in the classified pharmaceutical data;
• and
• marking the classified pharmaceutical data as unreliable in the knowledge database, thereby validating any fused knowledge created from the knowledge database, wherein the fused knowledge includes knowledge obtained from a plurality of domains from pharmaceutical industries fused into a multi-parameter output in a single parallel pass through the knowledge database.

An automated reasoning creation and analysis system, comprising in combination:

• an automated reasoning engine for acquiring domain specific knowledge from a plurality of pharmaceutical information sources, creating a semantic representation of the domain specific knowledge that meets a desired set of criteria, classifying pharmaceutical data from a knowledge database with the semantic representation, and providing a set of reasons for any classified pharmaceutical data, wherein the set of reasons are used to help interpret the classified pharmaceutical data, creating a further semantic representation of the domain specific knowledge, classifying pharmaceutical data from the knowledge database with the further semantic representation, determining with the set of reasons whether any classified pharmaceutical data includes data related to physical errors or biological errors, and if so, marking classified pharmaceutical data related to physical errors or biological errors as unreliable in the knowledge database, thereby validating any fused knowledge created from the knowledge database, and wherein the fused knowledge includes knowledge obtained from a plurality of domains from pharmaceutical industries fused into a multi-parameter output in a single parallel pass through the knowledge database;
• plurality of domain specific knowledge from a plurality of pharmaceutical information sources;
• and
• a knowledge database for storing raw experimental data and knowledge derived from raw pharmaceutical data.