Associating genes with gene ontology codes using a maximum entropy analysis of biomedical literature
Associating genes with gene ontology codes using a maximum entropy analysis of biomedical literature
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DOI：
10.1101/gr.199701
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发表时间：
2002-01-01
期刊：
影响因子：
7
通讯作者：
Altman, RB
中科院分区：
文献类型：
--
作者：
Raychaudhuri, S;Chang, JT;Altman, RB
在已发表的文献中描述了来自许多物种的数千种基因产物的功能表征。这些讨论不仅对于表征这些基因产物的功能，而且对于表征它们在其他生物中的同源物的功能都是非常有价值的。基因本体论（GO）致力于创建一个受控的术语，用于以更精确、可靠、计算机可读的方式标记基因功能。目前，用GO对基因功能进行最好的注释是由训练有素的生物学家进行的，他们阅读文献并选择适当的代码。在这项研究中，我们探讨了统计自然语言处理技术可用于分配GO代码的可能性。我们比较了三种文档分类方法（最大熵建模，朴素贝叶斯分类，最近邻分类）的问题相关联的一组GO代码（生物过程）的文献摘要，从而与摘要相关的基因。我们表明，最大熵建模优于其他方法，并实现了72%的准确性时，确定所有抽象中讨论的功能。最大熵方法提供了与性能良好相关的置信度度量。我们的结论是，统计方法可用于分配GO代码，并可能是有用的重新分配的困难任务，随着时间的推移，术语标准的发展。
Functional characterizations of thousands of gene products from many species are described in the published literature. These discussions are extremely Valuable for characterizing the functions not only of these gene products, but also of their homologs in other organisms. The Gene Ontology (GO) is ail effort to create a controlled terminology for labeling gene functions in a more precise, reliable, computer-readable manner. Currently, the best annotations of gene function with the GO are performed by highly trained biologists who read the literature and select appropriate codes. In this study, we explored the possibility that statistical natural language processing techniques can be used to assign GO codes. We compared three document classification methods (maximum entropy modeling, naive Bayes classification, and nearest-neighbor classification) to the problem of associating a set of GO codes (for biological process) to literature abstracts and thus to the genes associated with the abstracts. We showed that maximum entropy modeling outperforms the other methods and achieves ail accuracy of 72% when ascertaining the function discussed within ail abstract. The maximum entropy method provides confidence measures that correlate well with performance. We conclude that statistical methods may be used to assign GO codes and may be useful for the difficult task of reassignment as terminology standards evolve over time.