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首页> 美国卫生研究院文献>AMIA Annual Symposium Proceedings >Automated Metabolic Phenotyping of Cytochrome Polymorphisms Using PubMed Abstract Mining
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Automated Metabolic Phenotyping of Cytochrome Polymorphisms Using PubMed Abstract Mining

机译：利用PubMed抽象采矿技术自动进行细胞色素多态性的代谢表型分析

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摘要

Pharmacogenetics-related publications, which are increasing rapidly, provide important new pharmacogenetics knowledge. Automated approaches to extract information of new alleles and to identify their impact on metabolic phenotypes from publications are urgently needed to facilitate personalized medicine and improve clinical outcomes. Cytochrome polymorphisms, responsible for a wide variation of drug pharmacodynamics, individual efficacy and adverse effects, have significant potential for optimizing drug therapy. A few studies have addressed specialized efforts to automatically extract cytochrome polymorphisms and their characterizations regarding metabolic phenotypes from the literature. In this paper, we present a novel rule-based text-mining system to extract metabolic phenotypes of polymorphisms from PubMed abstracts with a focus on cytochrome P450. This system is promising as it achieved a precision of 85.71% in a preliminary proof-of-concept evaluation and is expected to automatically provide up–to-date metabolic information for cytochrome polymorphisms, which is critical to advance personalized medicine and improve clinical care.
机译：与药物遗传学有关的出版物正在迅速增加，提供了重要的新的药物遗传学知识。迫切需要一种自动化的方法来提取新等位基因的信息，并从出版物中识别其对代谢表型的影响，以促进个性化医学和改善临床结果。细胞色素多态性负责药物药效学，个人功效和不良反应的广泛变化，具有优化药物治疗的巨大潜力。一些研究已针对从文献中自动提取细胞色素多态性及其关于代谢表型的特征的专门研究。在本文中，我们提出了一种基于规则的新型文本挖掘系统，该系统可从PubMed摘要中提取多态性的代谢表型，重点是细胞色素P450。该系统在初步的概念验证评估中达到了85.71％的精度，因此有望实现这一目标，并有望为细胞色素多态性自动提供最新的代谢信息，这对于推进个性化医学和改善临床护理至关重要。

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