Automatic Extraction of Nanoparticle Properties Using Natural Language Processing: NanoSifter an Application to Acquire PAMAM Dendrimer Properties
Automatic Extraction of Nanoparticle Properties Using Natural Language Processing: NanoSifter an Application to Acquire PAMAM Dendrimer Properties
复制标题
使用自然语言处理自动提取纳米颗粒特性：NanoSifter 是获取 PAMAM 树枝状聚合物特性的应用程序
作者：
David E. Jones;Sean Igo;John F. Hurdle;J. Facelli
在这项研究中，我们展示了使用自然语言处理方法来提取，从纳米医学文献，数值的聚（酰胺胺）树枝状聚合物的生物医学特性条款。我们已经开发了一种方法，用于提取这些值的属性从纳米粒子本体，使用文本工程的一般架构和一个近乎新的信息提取系统。我们还创建了一种方法，用于将识别的数值与其相应的树枝状聚合物属性相关联，称为NanoSifter。我们证明，我们的系统可以正确地提取数字值的树枝状聚合物的性质，在癌症治疗文献中报道的高召回率，精度和f-措施。微平均召回率为0.99，精确度为0.84，f-测量值为0.91。同样，宏观平均召回率为0.99，精确度为0.87，f-测量值为0.92。据我们所知，这些结果是文本挖掘的第一个应用程序，以提取和关联的树枝状聚合物的属性条款和相应的数值。
In this study, we demonstrate the use of natural language processing methods to extract, from nanomedicine literature, numeric values of biomedical property terms of poly(amidoamine) dendrimers. We have developed a method for extracting these values for properties taken from the NanoParticle Ontology, using the General Architecture for Text Engineering and a Nearly-New Information Extraction System. We also created a method for associating the identified numeric values with their corresponding dendrimer properties, called NanoSifter. We demonstrate that our system can correctly extract numeric values of dendrimer properties reported in the cancer treatment literature with high recall, precision, and f-measure. The micro-averaged recall was 0.99, precision was 0.84, and f-measure was 0.91. Similarly, the macro-averaged recall was 0.99, precision was 0.87, and f-measure was 0.92. To our knowledge, these results are the first application of text mining to extract and associate dendrimer property terms and their corresponding numeric values.
影响因子：
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作者：
Kris C Wood;S. Little;R. Langer;P. Hammond
通讯作者：
Kris C Wood;S. Little;R. Langer;P. Hammond
影响因子：
2.1
作者：
Garten Y;Coulet A;Altman RB
通讯作者：
Altman RB