最佳匹配图编辑的启发式算法
Heuristic Algorithms for Best Match Graph Editing
摘要：
最佳匹配图（BMG）是一类自然出现在数学遗传学中的有色有向图，可以在基因序列之间的相似性度量的帮助下近似，尽管并非没有错误。相应的图形编辑问题可以作为一种纠错手段。由于BMG的弧集修改问题是NP完全的，如果BMG被用于生物序列数据的实际分析，则需要有效的算法。由于BMG有一个表征的一致性的一组特定的根三元组，我们认为是操作的三元组。作为一种替代方案，我们表明，有一个密切的连接到一组分区问题，导致一类自上而下的递归算法，类似于Aho的超树算法，并产生BMG编辑算法是一致的，在这个意义上，他们离开BMG不变。广泛的基准测试表明，社区检测算法的分区步骤执行最好的BMG编辑。
英文摘要：
Best match graphs (BMGs) are a class of colored digraphs that naturally appear in mathematical phylogenetics and can be approximated with the help of similarity measures between gene sequences, albeit not without errors. The corresponding graph editing problem can be used as a means of error correction. Since the arc set modification problems for BMGs are NP-complete, efficient heuristics are needed if BMGs are to be used for the practical analysis of biological sequence data. Since BMGs have a characterization in terms of consistency of a certain set of rooted triples, we consider heuristics that operate on triple sets. As an alternative, we show that there is a close connection to a set partitioning problem that leads to a class of top-down recursive algorithms that are similar to Aho's supertree algorithm and give rise to BMG editing algorithms that are consistent in the sense that they leave BMGs invariant. Extensive benchmarking shows that community detection algorithms for the partitioning steps perform best for BMG editing.
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