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    基于文本挖掘的精子发生各阶段的相关基因/蛋白名称提取

    Extraction of Gene/Protein Names Involved in Each Stage of Spermatogenesis Based on Literature Mining

    • 摘要: 精子发生是雄性哺乳动物生命活动中一个重要的生物学过程,该过程的每一个阶段都有众多基因/蛋白参与并发挥功能.相关基因/蛋白出现异常是导致男性不育症的主要诱因,但这些基因/蛋白的信息大都分散在科研文献中,而人工从海量文献中提取这些基因/蛋白名称费时费力,因此,基于文本挖掘技术,提出了自动提取精子发生过程各个阶段中发挥作用的基因/蛋白名称的策略.首先比较了3种不同算法在不同词条数目下的分类效果,并确定用支持向量机(support vector machine, SVM)算法对相关文本按照精子发生过程的3阶段分类,然后建立适当的信息提取和筛选方法,从文献摘要中提取每个阶段中的基因/蛋白名称.最后,通过与人工提取的基因/蛋白名称进行比较验证,提取结果的正确率为71.9%,证明了提取策略的可行性.

       

      Abstract: Spermatogenesis is an important bioprocess in the lifetime of male mammalians, which has deep effect on mammals reproduction. Abnormal spermatogenesis is a major cause of male infertility, however treatments for this are limited. Characterizing the genes/proteins involved in spermatogenesis is fundamental to understand the mechanisms underlying this biological process and to develop treatments for the problems in spermatogenesis. However, most crucial information of spermatogenesis-related genes/proteins scatters in vast amount of research articles, so manually curation of these genes/proteins could be a time-consuming task. In this paper, a novel strategy is proposed to automatically extract the names of spermatogenesis-related genes/proteins, which function in different stages of spermatogenesis based on literature mining. Firstly, it compares three different algorithms performance on different terms and applys an SVM classifier trained with a manually prepared dataset to classify spermatogenesis-related texts into three classes in accordance with the three stages of spermatogenesis. Then, integrating expert knowledge and grammar rules, it recongnizes and extracts the gene/protein names of each spermatogenesis stage with high confidence. Finally, a manually curation test dataset is used to test the performance of this strategy, and the strategy gets an accuracy of 71.9%, which verifys the reliability of proposed method and proves the value of application.

       

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