Mining Context-Specific Rules from the Literature for Virtual Human Model Simulation

Computer-based virtual human model is believed to be the promising solution for drug response identification. Literature mining is competitive method to extract those biological rules for human model simulation, since existing public databases provide only limited amount of information applicable for the simulation. Here we propose the method for mining context-specific rules from the literature, for future application to virtual human model simulation. Integrating the existing biological databases, we have constructed formalized ontology. From the PubMed literature, we have tagged 11 distinct types of biological entities using both of conditional random field (CRF) and dictionary based Named Entity Recognition (NER). Recognized named entities were normalized and mapped to formalized ontology. Context-specific biological rules between named entities, characterized by increase/decrease features, were extracted by pattern-based method utilizing regular expression. As the result, we have obtained the organ-context specific biological rules. Further researches on enhanced rule and context extraction will be followed.