从文献中挖掘上下文特定规则用于虚拟人体模型仿真

Kwangmin Kim, Sejoon Lee, Kyunghyun Park, Dongjin Jang, Doheon Lee
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引用次数: 0

摘要

基于计算机的虚拟人体模型被认为是药物反应识别的有前途的解决方案。文献挖掘是提取人体模型仿真的生物学规律的一种有竞争力的方法,因为现有的公共数据库提供的适用于仿真的信息有限。在这里,我们提出了从文献中挖掘上下文特定规则的方法,以便将来应用于虚拟人体模型仿真。结合现有的生物数据库,构建了形式化的本体。从PubMed文献中,我们使用条件随机场(CRF)和基于字典的命名实体识别(NER)标记了11种不同类型的生物实体。识别的命名实体被规范化并映射到形式化的本体。采用基于模式的方法,利用正则表达式提取命名实体之间以增减特征为特征的上下文特定生物规则。结果,我们获得了器官环境特异性的生物学规律。后续将进一步研究增强的规则和上下文提取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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