[Major revision of the allergen database for food safety (ADFS) and validation of the motif-based allergenicity prediction tool].

Q4 Medicine Bulletin of National Institute of Health Sciences Pub Date : 2009-01-01
Ryosuke Nakamura, Rika Nakamura, Reiko Teshima
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引用次数: 0

Abstract

We have been maintaining an integral web server system, the Allergen Database for Food Safety (ADFS), since 2005 (http://allergen.nihs.go.jp/ADFS/). Recently, a group at the University of Nebraska-Lincoln released a new version of an allergen database, AllergenOnline. This database includes more than 1,300 allergens, all of which have been peer-reviewed by an international board of allergology experts. Here, we have totally revised the dataset of the ADFS by comparing it with that of AllergenOnline to improve the reliability of our allergen data. Moreover, the performance of our web-based tool for predicting new allergens (motif-based method), which was developed according to a theory proposed by Stadler & Stadler (2003), was validated using three methods. As a result of the integration of this allergen data, the number of (iso)allergens in the ADFS has increased to 1340, and epitope information is now available for 76 allergens. Using model datasets, the precision, recall, and specificity of our motif-based allergenicity prediction tool was proved to be 100.0%, 99.4%, and 100.0%, respectively. These results were similar to those for the original motif-based prediction model that was previously reported and are much better than those of the method recommended by FAO/WHO, especially with regard to the precision of predictions.

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[食品安全过敏原数据库(ADFS)的重大修订和基于基序的过敏原预测工具的验证]。
自2005年以来,我们一直在维护一个完整的网络服务器系统,即食品安全过敏原数据库(ADFS) (http://allergen.nihs.go.jp/ADFS/)。最近,内布拉斯加大学林肯分校的一个小组发布了一个新版本的过敏原数据库——AllergenOnline。该数据库包括1300多种过敏原,所有这些过敏原都经过了国际过敏学专家委员会的同行评审。在此,我们对ADFS数据集进行了全面修改,并与AllergenOnline的数据集进行了比较,以提高我们的过敏原数据的可靠性。此外,根据Stadler & Stadler(2003)提出的理论开发的基于网络的预测新过敏原的工具(基于基序的方法)的性能使用三种方法进行了验证。由于这些过敏原数据的整合,ADFS中的(iso)过敏原数量增加到1340个,现在可以获得76个过敏原的表位信息。使用模型数据集,我们基于基序的过敏原预测工具的准确率、召回率和特异性分别为100.0%、99.4%和100.0%。这些结果与先前报道的原始基于基序的预测模型的结果相似,并且比粮农组织/世卫组织推荐的方法的结果要好得多,特别是在预测精度方面。
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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