Extending the mutual information measure to rank inferred literature relationships.
Extending the mutual information measure to rank inferred literature relationships.
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DOI：
10.1186/1471-2105-5-145
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发表时间：
2004-10-07
影响因子：
3
通讯作者：
Wren JD
中科院分区：
文献类型：
--
作者：
Wren JD
在同行评审的文献中，直到两个事物之间的共性变得明显之前，它们之间的关联并不总是被认识到。这些共性可以为推断以前未知的新关系提供理由，并且是大多数基于观察的假设形成的基础。事实证明，问题的关键不是找到可推断的关联，考虑到基于文献的关联产生的无标度网络，这种关联非常丰富，而是确定哪些关联具有信息性。互信息度量 (MIM) 是一种行之有效的方法，用于衡量关联的信息量，但仅限于直接（即可观察的）关联。在这里，我们尝试通过使用共享关联的 MIM 将互信息的计算扩展到间接（即可推断）关联。 MEDLINE 中发现的一般研究兴趣的对象（例如基因、疾病、表型、药物、本体类别）用于创建用于评估的关联网络。互信息计算可以有效地扩展到隐含关系和通过随机词网络分析估计的显着性截止值。在测试的模型中，发现共享最小 MIM (MMIM) 模型与观察到的已知关联的强度和频率相关性最好。使用三个测试用例，MMIM 方法倾向于将更具体的关系排名高于计算网络内共享关系的数量。
Within the peer-reviewed literature, associations between two things are not always recognized until commonalities between them become apparent. These commonalities can provide justification for the inference of a new relationship where none was previously known, and are the basis of most observation-based hypothesis formation. It has been shown that the crux of the problem is not finding inferable associations, which are extraordinarily abundant given the scale-free networks that arise from literature-based associations, but determining which ones are informative. The Mutual Information Measure (MIM) is a well-established method to measure how informative an association is, but is limited to direct (i.e. observable) associations. Herein, we attempt to extend the calculation of mutual information to indirect (i.e. inferable) associations by using the MIM of shared associations. Objects of general research interest (e.g. genes, diseases, phenotypes, drugs, ontology categories) found within MEDLINE are used to create a network of associations for evaluation. Mutual information calculations can be effectively extended into implied relationships and a significance cutoff estimated from analysis of random word networks. Of the models tested, the shared minimum MIM (MMIM) model is found to correlate best with the observed strength and frequency of known associations. Using three test cases, the MMIM method tends to rank more specific relationships higher than counting the number of shared relationships within a network.
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DOI：
10.1046/j.1474-8673.2002.00242.x
发表时间：
2002-02-01
期刊：
Autonomic & Autacoid Pharmacology
影响因子：
--
作者：
Li, J. Q.;Jia, Y. X.;Sasaki, H.
通讯作者：
Sasaki, H.
影响因子：
14.9
作者：
Hamosh, A;Scott, AF;McKusick, VA
通讯作者：
McKusick, VA
影响因子：
5.8
作者：
Wren, JD;Bekeredjian, R;Garner, HR
通讯作者：
Garner, HR
影响因子：
5.9
作者：
DIGIACOMO, RA;KREMER, JM;SHAH, DM
通讯作者：
SHAH, DM
影响因子：
30.8
作者：
Jenssen, TK;Lægreid, A;Hovig, E
通讯作者：
Hovig, E