The Mouse Age Phenome Knowledgebase and Disease-Specific Inter-Species Age Mapping
The Mouse Age Phenome Knowledgebase and Disease-Specific Inter-Species Age Mapping
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DOI：
10.1371/journal.pone.0081114
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发表时间：
2013-12-03
期刊：
影响因子：
3.7
通讯作者：
Rubin, Eitan
中科院分区：
文献类型：
--
作者：
Geifman, Nophar;Rubin, Eitan
背景：小鼠和人类之间的相似性导致了许多人类疾病的小鼠模型的产生。然而，物种之间的差异往往导致小鼠不能作为人类疾病的临床前模型。在降低小鼠模型对人类疾病的预测性方面，一个可能起作用的差异是年龄。尽管年龄在医学上扮演着重要的角色，但在考虑小鼠模型时，它往往被随意考虑。方法：我们开发了小鼠年龄表型知识库，其中包含关于小鼠年龄相关表型模式的知识。使用文本挖掘技术，在知识库中广泛填充了从文献派生的数据。然后，我们通过比较两个物种中887种疾病的年龄分布模式，在人和小鼠的年龄之间进行映射。结果：通过文本挖掘管道生成的9800多个实例填充了知识库。人工评估数据的质量，发现其准确度很高(估计精确度>；86%)。此外，将老鼠中年龄模式相似的疾病组合在一起，会产生反映实际生物医学知识的集群。使用这些数据，我们匹配了老鼠和人类的年龄分布模式，通过改变其中任何一种模式来考虑年龄差异。223种疾病有较高的相关性(r(2)>；0.5)。结果清楚地表明，不同疾病之间的年龄映射存在差异：白血病患者的30岁年龄映射为120天，而贫血患者的年龄映射为295天。基于这些结果，我们生成了一张公开可用的小鼠到人类的年龄图。结论：我们在这里介绍了小鼠APK的发展，它的种群和来自文献的数据，以及它在绘制223种疾病的小鼠和人类年龄图中的应用。这些结果代表了在生物医学研究中弥合人类和老鼠之间的差距所采取的进一步步骤。
Background: Similarities between mice and humans lead to generation of many mouse models of human disease. However, differences between the species often result in mice being unreliable as preclinical models for human disease. One difference that might play a role in lowering the predictivity of mice models to human diseases is age. Despite the important role age plays in medicine, it is too often considered only casually when considering mouse models.Methods: We developed the mouse-Age Phenotype Knowledgebase, which holds knowledge about age-related phenotypic patterns in mice. The knowledgebase was extensively populated with literature-derived data using text mining techniques. We then mapped between ages in humans and mice by comparing the age distribution pattern for 887 diseases in both species.Results: The knowledgebase was populated with over 9800 instances generated by a text-mining pipeline. The quality of the data was manually evaluated, and was found to be of high accuracy (estimated precision >86%). Furthermore, grouping together diseases that share similar age patterns in mice resulted in clusters that mirror actual biomedical knowledge. Using these data, we matched age distribution patterns in mice and in humans, allowing for age differences by shifting either of the patterns. High correlation (r(2) >.0.5) was found for 223 diseases. The results clearly indicate a difference in the age mapping between different diseases: age 30 years in human is mapped to 120 days in mice for Leukemia, but to 295 days for Anemia. Based on these results we generated a mice-to-human age map which is publicly available.Conclusions: We present here the development of the mouse-APK, its population with literature-derived data and its use to map ages in mice and human for 223 diseases. These results present a further step made to bridging the gap between humans and mice in biomedical research.