Tunable machine vision-based strategy for automated annotation of chemical databases.
Tunable machine vision-based strategy for automated annotation of chemical databases.
复制标题
基于可调谐机器视觉的化学数据库自动注释策略。
DOI：
10.1021/ci900029v
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发表时间：
2009
影响因子：
5.6
通讯作者：
Saitou,Kazuhiro
中科院分区：
文献类型：
--
作者：
Park,Jungkap;Rosania,GusR;Saitou,Kazuhiro
我们提出了一个可调的，基于机器视觉的策略，用于自动注释虚拟小分子数据库。所提出的策略是基于使用的机器视觉为基础的工具，用于提取结构图的研究文章，并将它们转换成连接表，一个虚拟的“化学专家”系统，用于筛选转换后的结构的基础上的可调水平的估计转换精度，和一个基于片段的措施，用于计算分子间的相似性。对于注释，使用转换的结构和虚拟小分子数据库中的条目之间的计算的化学相似性来建立链接。整体注释性能可以通过调整估计的转换准确度的截止阈值来调整。我们进行了一个注释测试，试图将PubMed中注册的121篇期刊文章链接到PubChem中的条目，PubChem是最大的，可公开访问的化学数据库。两种情况下的测试进行了比较，看看如何整体注释性能的转换结构的估计精度的不同阈值水平的影响。我们的工作表明，超过45%的文章可能与PubChem数据库中的条目有真正的正链接，在两个测试中都有很好的召回率和准确率。此外，我们说明了化学专家系统，它可以筛选转换结构的基础上可调水平的估计转换精度是影响整体注释性能的一个关键因素。我们建议这种基于机器视觉的策略可以与文本挖掘方法相结合，以促进从科学文献中提取有关化学结构的上下文科学知识。
We present a tunable, machine vision-based strategy for automated annotation of virtual small molecule databases. The proposed strategy is based on the use of a machine vision-based tool for extracting structure diagrams in research articles and converting them into connection tables, a virtual “Chemical Expert” system for screening the converted structures based on the adjustable levels of estimated conversion accuracy, and a fragment-based measure for calculating intermolecular similarity. For annotation, calculated chemical similarity between the converted structures and entries in a virtual small molecule database is used to establish the links. The overall annotation performances can be tuned by adjusting the cutoff threshold of the estimated conversion accuracy. We perform an annotation test which attempts to link 121 journal articles registered in PubMed to entries in PubChem which is the largest, publicly accessible chemical database. Two cases of tests are performed, and their results are compared to see how the overall annotation performances are affected by the different threshold levels of the estimated accuracy of the converted structure. Our work demonstrates that over 45% of the articles could have true positive links to entries in the PubChem database with promising recall and precision rates in both tests. Furthermore, we illustrate that the Chemical Expert system which can screen converted structures based on the adjustable levels of estimated conversion accuracy is a key factor impacting the overall annotation performance. We propose that this machine vision-based strategy can be incorporated with the text-mining approach to facilitate extraction of contextual scientific knowledge about a chemical structure, from the scientific literature.