Using deep learning to recognize biomedical entities

Xuemin Yang, Zhifei Zhang, R. Yang, Daoyu Huang, Geng Yang, Lejun Gong
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引用次数: 3

Abstract

With the rapid growth of the high-throughput biological technology, it brings biomedical big omics' data containing literature and annotated data. Especially, a wealth of relevant information exists in various types of biomedical literature. Text mining has emerged as a potential solution to achieve knowledge for bridging between the free text and structured representation of biomedical information. In this work, we used deep learning to recognize biomedical entities. We obtained 84.0% precision, 69.5% recall, and 76.1% F-score aiming at the GENIA corpus, and obtained 91.3% precision, 91.1% recall, and 91.2% F-score aiming at the BioCreAtIvE II Gene Mention corpus. Experimental results show that our proposed approach is promising for developing biomedical text mining technology in biomedical entity recognition.
使用深度学习来识别生物医学实体
随着高通量生物技术的快速发展,带来了生物医学大组学包含文献和注释数据的数据。特别是在各种类型的生物医学文献中存在着丰富的相关信息。文本挖掘已经成为一种潜在的解决方案,可以在自由文本和生物医学信息的结构化表示之间架起桥梁。在这项工作中,我们使用深度学习来识别生物医学实体。针对GENIA语料库,我们获得了84.0%的准确率、69.5%的召回率和76.1%的F-score;针对BioCreAtIvE II基因提及语料库,我们获得了91.3%的准确率、91.1%的召回率和91.2%的F-score。实验结果表明,本文提出的方法对生物医学文本挖掘技术在生物医学实体识别领域的发展具有一定的指导意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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