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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Coupling Graphs, Efficient Algorithmsand B-Cell Epitope Prediction
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Coupling Graphs, Efficient Algorithmsand B-Cell Epitope Prediction

机译：耦合图，高效算法和B细胞表位预测

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摘要

Coupling graphs are newly introduced in this paper to meet many application needs particularly in the field of bioinformatics. A coupling graph is a two-layer graph complex, in which each node from one layer of the graph complex has at least one connection with the nodes in the other layer, and vice versa. The coupling graph model is sufficiently powerful to capture strong and inherent associations between subgraph pairs in complicated applications. The focus of this paper is on mining algorithms of frequent coupling subgraphs and bioinformatics application. Although existing frequent subgraph mining algorithms are competent to identify frequent subgraphs from a graph database, they perform poorly on frequent coupling subgraph mining because they generate many irrelevant subgraphs. We propose a novel graph transformation technique to transform a coupling graph into a generic graph. Based on the transformed coupling graphs, existing graph mining methods are then utilized to discover frequent coupling subgraphs. We prove that the transformation is precise and complete and that the restoration is reversible. Experiments carried out on a database containing 10,511 coupling graphs show that our proposed algorithm reduces the mining time very much in comparison with the existing subgraph mining algorithms. Moreover, we demonstrate the usefulness of frequent coupling subgraphs by applying our algorithm to make accurate predictions of epitopes in antibody-antigen binding.
机译：本文新引入了耦合图，以满足许多应用需求，特别是在生物信息学领域。耦合图是两层图复合体，其中图复合体中一层的每个节点与另一层中的节点至少具有一个连接，反之亦然。耦合图模型足够强大，可以捕获复杂应用程序中子图对之间的强大关联和固有关联。本文的重点是频繁耦合子图的挖掘算法和生物信息学的应用。尽管现有的频繁子图挖掘算法能够从图形数据库中识别频繁子图，但由于它们会生成许多不相关的子图，因此它们在频繁耦合子图挖掘中的性能较差。我们提出了一种新颖的图变换技术，将耦合图变换为通用图。基于转换后的耦合图，然后利用现有的图挖掘方法发现频繁的耦合子图。我们证明该转换是精确且完整的，并且恢复是可逆的。在包含10,511个耦合图的数据库上进行的实验表明，与现有的子图挖掘算法相比，我们提出的算法大大减少了挖掘时间。此外，我们通过应用我们的算法对抗体-抗原结合中的表位进行准确的预测，证明了频繁偶联子图的有用性。

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