Context-driven automatic subgraph creation for literature-based discovery.
Context-driven automatic subgraph creation for literature-based discovery.
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DOI：
10.1016/j.jbi.2015.01.014
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发表时间：
2015-04
影响因子：
4.5
通讯作者：
Bodenreider O
中科院分区：
文献类型：
--
作者：
Cameron D;Kavuluru R;Rindflesch TC;Sheth AP;Thirunarayan K;Bodenreider O
基于文献的发现（Literature-baseddiscovery，LBD）的特点是在非交互的科学文献中发现隐藏的关联。LBD的先前方法包括使用：1）领域专业知识和结构化背景知识来手动过滤和探索文献，2）分布统计和图论测量来对感兴趣的连接进行排名，以及3）帮助消除虚假连接的统计学。然而，LBD的手动方法是不可扩展的，并且纯粹的分布式方法可能不足以深入了解理解不清楚的关联的含义。虽然一些基于图形的方法有可能阐明关联，但它们的有效性尚未得到充分证明。仍然需要相当程度的先验知识、启发式方法和手动过滤。在本文中，我们实现和评估一个上下文驱动的，自动子图创建方法，捕捉多方面的复杂的生物医学概念之间的关联，以促进LBD。给定一对概念，我们的方法自动生成子图的排名列表，这些子图提供了这些概念之间的信息和潜在的未知关联。为了生成子图，首先收集包含两个指定概念（A，C）之一的所有MEDLINE文章的集合。然后使用从MEDLINE文章中自动提取的二元关系或断言（称为语义谓词）来创建带标签的有向谓词图。在这个预测图中，路径被表示为语义预测的序列。然后将层次凝聚聚类（HAC）算法应用于由两个概念（A，C）限定的聚类路径。HAC依赖于通过医学主题标题（MeSH）描述符捕获的隐式语义和来自MeSH层次结构的显式语义来进行聚类。超过语义相关性阈值的路径基于它们的共享上下文被聚类到子图中。最后，自动生成的聚类作为子图的排名列表提供。使用这种方法生成的子图促进了9个现有科学发现中的8个的重新发现。特别是，它们直接（或间接）导致了A和C术语之间的几个中间体（或B概念）的恢复，同时也提供了对关联意义的见解。这种意义来源于概念之间的谓词，以及MEDLINE中语义谓词的出处。此外，通过在不同的主题维度（如细胞活性、药物治疗和组织功能）上生成子图，该方法可以更广泛地理解概念之间复杂关联的性质。最后，在确定子图兴趣度的统计评价中，观察到平均仅在MEDLINE中约4篇文章中提到任意关联。这些结果表明，利用手动分配的MeSH描述符提供的隐式和显式语义是一种有效的表示捕捉复杂的关联，沿着多个主题维度在LBD的情况下的底层上下文。
Literature-based discovery (LBD) is characterized by uncovering hidden associations in non-interacting scientific literature. Prior approaches to LBD include use of: 1) domain expertise and structured background knowledge to manually filter and explore the literature, 2) distributional statistics and graph-theoretic measures to rank interesting connections, and 3) heuristics to help eliminate spurious connections. However, manual approaches to LBD are not scalable and purely distributional approaches may not be sufficient to obtain insights into the meaning of poorly understood associations. While several graph-based approaches have the potential to elucidate associations, their effectiveness has not been fully demonstrated. A considerable degree of a priori knowledge, heuristics, and manual filtering is still required. In this paper we implement and evaluate a context-driven, automatic subgraph creation method that captures multifaceted complex associations between biomedical concepts to facilitate LBD. Given a pair of concepts, our method automatically generates a ranked list of subgraphs, which provide informative and potentially unknown associations between such concepts. To generate subgraphs, the set of all MEDLINE articles that contain either of the two specified concepts (A, C) are first collected. Then binary relationships or assertions, which are automatically extracted from the MEDLINE articles, called semantic predications, are used to create a labeled directed predications graph. In this predications graph, a path is represented as a sequence of semantic predications. The hierarchical agglomerative clustering (HAC) algorithm is then applied to cluster paths that are bounded by the two concepts (A, C). HAC relies on implicit semantics captured through Medical Subject Heading (MeSH) descriptors, and explicit semantics from the MeSH hierarchy, for clustering. Paths that exceed a threshold of semantic relatedness are clustered into subgraphs based on their shared context. Finally, the automatically generated clusters are provided as a ranked list of subgraphs. The subgraphs generated using this approach facilitated the rediscovery of 8 out of 9 existing scientific discoveries. In particular, they directly (or indirectly) led to the recovery of several intermediates (or B-concepts) between A- and C-terms, while also providing insights into the meaning of the associations. Such meaning is derived from predicates between the concepts, as well as the provenance of the semantic predications in MEDLINE. Additionally, by generating subgraphs on different thematic dimensions (such as Cellular Activity, Pharmaceutical Treatment and Tissue Function), the approach may enable a broader understanding of the nature of complex associations between concepts. Finally, in a statistical evaluation to determine the interestingness of the subgraphs, it was observed that an arbitrary association is mentioned in only approximately 4 articles in MEDLINE on average. These results suggest that leveraging the implicit and explicit semantics provided by manually assigned MeSH descriptors is an effective representation for capturing the underlying context of complex associations, along multiple thematic dimensions in LBD situations.