MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature.
MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature.
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DOI：
10.1186/1471-2105-12-471
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发表时间：
2011-12-14
影响因子：
3
通讯作者：
Hsu WL
中科院分区：
文献类型：
--
作者：
Fang YC;Lai PT;Dai HJ;Hsu WL
DNA甲基化被认为是癌症诊断和治疗的潜在生物标志物。最近的一些科学研究已经确定了基因甲基化异常与癌症发生之间的关系。在以前的工作中，我们使用共现来挖掘这些关联，并编译了MeInfoText 1.0数据库。为了减少人工筛选的工作量，提高关系提取的准确性，我们现在开发了MeInfoText 2.0，它使用基于机器学习的方法来提取基因甲基化与癌症的关系。两个最大熵模型被训练来预测基因甲基化异常是否与文献中提到的任何类型的癌症有关。经过10次交叉验证后，两种模型的平均准确率/召回率分别为94.7/90.1和91.8/90%。MeInfoText 2.0提供了不同类型人类癌症的基因甲基化情况。所提取的与最大概率、证据句子和特定基因信息的关系也是可检索的。该数据库可在http://bws.iis.sinica.edu.tw:8081/MeInfoText2/.上获得之前的版本MeInfoText是通过使用关联规则开发的，而MeInfoText 2.0基于一个新的框架，该框架结合了机器学习、词典查找和模式匹配来提取表观遗传学信息。实验结果表明，MeInfoText 2.0在很多方面都优于现有的工具。据我们所知，这是第一个使用混合方法提取基因甲基化与癌症关系的研究。这也是建立基因甲基化与癌症关系语料库的第一次尝试。
DNA methylation is regarded as a potential biomarker in the diagnosis and treatment of cancer. The relations between aberrant gene methylation and cancer development have been identified by a number of recent scientific studies. In a previous work, we used co-occurrences to mine those associations and compiled the MeInfoText 1.0 database. To reduce the amount of manual curation and improve the accuracy of relation extraction, we have now developed MeInfoText 2.0, which uses a machine learning-based approach to extract gene methylation-cancer relations. Two maximum entropy models are trained to predict if aberrant gene methylation is related to any type of cancer mentioned in the literature. After evaluation based on 10-fold cross-validation, the average precision/recall rates of the two models are 94.7/90.1 and 91.8/90% respectively. MeInfoText 2.0 provides the gene methylation profiles of different types of human cancer. The extracted relations with maximum probability, evidence sentences, and specific gene information are also retrievable. The database is available at http://bws.iis.sinica.edu.tw:8081/MeInfoText2/. The previous version, MeInfoText, was developed by using association rules, whereas MeInfoText 2.0 is based on a new framework that combines machine learning, dictionary lookup and pattern matching for epigenetics information extraction. The results of experiments show that MeInfoText 2.0 outperforms existing tools in many respects. To the best of our knowledge, this is the first study that uses a hybrid approach to extract gene methylation-cancer relations. It is also the first attempt to develop a gene methylation and cancer relation corpus.
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影响因子：
3
作者：
Tsai RT;Wu SH;Chou WC;Lin YC;He D;Hsiang J;Sung TY;Hsu WL
通讯作者：
Hsu WL
影响因子：
14.9
作者：
Ongenaert M;Van Neste L;De Meyer T;Menschaert G;Bekaert S;Van Criekinge W
通讯作者：
Van Criekinge W
影响因子：
7.3
作者：
Lehmann, U.;Hasemeier, B.;Kreipe, H.
通讯作者：
Kreipe, H.
影响因子：
9.5
作者：
Spasic, I;Ananiadou, S;Kumar, A
通讯作者：
Kumar, A
影响因子：
6.5
作者：
Sung, Cheng-Lung;Lee, Cheng-Wei;Hsu, Wen-Lian
通讯作者：
Hsu, Wen-Lian