Knowledge discovery by automated identification and ranking of implicit relationships
Knowledge discovery by automated identification and ranking of implicit relationships
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DOI：
10.1093/bioinformatics/btg421
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发表时间：
2004-02-12
期刊：
影响因子：
5.8
通讯作者：
Garner, HR
中科院分区：
文献类型：
--
作者：
Wren, JD;Bekeredjian, R;Garner, HR
动机：新的关系往往隐含在现有的信息中，但出版文献的数量和增长限制了个人可以完成的分析范围。我们的目标是开发和测试一种计算方法来识别科学报告中的关系，这样就可以找出不相关项目之间的大量关系，并根据它们作为一个集合的潜在相关性进行统计排名。我们首先构建了一个生物医学研究兴趣的“对象”之间的尝试性关系网络（例如基因、疾病、表型、化学物质），通过在所有电子版MEDLINE记录中识别其共同出现。然后，两个不相关对象共享的关系根据随机网络模型进行排名，以估计任何给定分组的统计显著性。当与已知的关系进行比较时，我们发现这种排名与对象共现的概率和频率相关，表明该方法非常适合基于现有的共享关系发现新的关系。为了测试这一点，我们确定了化合物的共同关系预测他们可能会影响心脏肥大的发展和/或进展。当在啮齿动物模型中进行实验室测试时，发现氯丙嗪可以减缓心脏肥大的进展。
Motivation: New relationships are often implicit from existing information, but the amount and growth of published literature limits the scope of analysis an individual can accomplish. Our goal was to develop and test a computational method to identify relationships within scientific reports, such that large sets of relationships between unrelated items could be sought out and statistically ranked for their potential relevance as a set.Results: We first construct a network of tentative relationships between 'objects' of biomedical research interest (e.g. genes, diseases, phenotypes, chemicals) by identifying their co-occurrences within all electronically available MEDLINE records. Relationships shared by two unrelated objects are then ranked against a random network model to estimate the statistical significance of any given grouping. When compared against known relationships, we find that this ranking correlates with both the probability and frequency of object co-occurrence, demonstrating the method is well suited to discover novel relationships based upon existing shared relationships. To test this, we identified compounds whose shared relationships predicted they might affect the development and/or progression of cardiac hypertrophy. When laboratory tests were performed in a rodent model, chlorpromazine was found to reduce the progression of cardiac hypertrophy.