RANKING RELATIONS USING ANALOGIES IN BIOLOGICAL AND INFORMATION NETWORKS.
RANKING RELATIONS USING ANALOGIES IN BIOLOGICAL AND INFORMATION NETWORKS.
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DOI：
10.1214/09-aoas321
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发表时间：
2010-08-03
期刊：
影响因子：
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通讯作者：
Airoldi EM
中科院分区：
文献类型：
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作者：
Silva R;Heller K;Ghahramani Z;Airoldi EM
类比推理从根本上依赖于学习和概括对象之间关系的能力。我们开发了一种关系学习的方法，给定一组对象对S = {A（1）：B（1），A（2）：B（2），.，A（N）：B（N）}，测量其他对A：B与集合S的匹配程度。我们的工作解决了以下问题：对象A和B之间的关系是否类似于S中的关系？这样的问题在信息检索中特别相关，其中研究者可能想要搜索与感兴趣的查询集匹配的类似对象对。有许多方法可以使对象相关，使得测量类比的任务非常具有挑战性。我们的方法结合了相似性度量函数空间与贝叶斯分析产生的排名。它需要包含感兴趣对象的特征的数据和指定存在哪些关系的链接矩阵;不需要此类关系的其他属性。我们说明了我们的方法在文本分析和信息网络上的潜力。详细讨论了发现蛋白质对之间的功能相互作用的应用程序，在那里我们表明，我们的方法可以在实践中工作，即使提供了一个小的蛋白质对。
Analogical reasoning depends fundamentally on the ability to learn and generalize about relations between objects. We develop an approach to relational learning which, given a set of pairs of objects S = {A(1) : B(1), A(2) : B(2), …, A(N) : B(N)}, measures how well other pairs A : B fit in with the set S. Our work addresses the following question: is the relation between objects A and B analogous to those relations found in S? Such questions are particularly relevant in information retrieval, where an investigator might want to search for analogous pairs of objects that match the query set of interest. There are many ways in which objects can be related, making the task of measuring analogies very challenging. Our approach combines a similarity measure on function spaces with Bayesian analysis to produce a ranking. It requires data containing features of the objects of interest and a link matrix specifying which relationships exist; no further attributes of such relationships are necessary. We illustrate the potential of our method on text analysis and information networks. An application on discovering functional interactions between pairs of proteins is discussed in detail, where we show that our approach can work in practice even if a small set of protein pairs is provided.