Automatic Parameter Learning for Multiple Local Network Alignment
Automatic Parameter Learning for Multiple Local Network Alignment
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DOI：
10.1089/cmb.2009.0099
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发表时间：
2009-08-01
影响因子：
1.7
通讯作者：
Batzoglou, Serafim
中科院分区：
文献类型：
--
作者：
Flannick, Jason;Novak, Antal;Batzoglou, Serafim
我们开发了Graemlin 2.0，这是一种新的多网络对齐器，它具有(1)一种新的多阶段本地网络对齐方法；(2)一种新的评分函数，可以使用多个网络比对的任意特征，如蛋白质缺失、蛋白质重复、蛋白质突变和相互作用损失；(3)参数学习算法，该算法使用已知网络对齐的训练集来学习我们的评分函数的参数，从而使其适应任何网络集；(4)使用我们的评分函数在线性时间内找到近似的多个网络对齐的算法。我们测试了Graemlin 2.0在蛋白质相互作用网络上的准确性，这些网络来自于完好无损、DIP和斯坦福网络数据库。我们表明，在这些数据集上，Graemlin 2.0比现有的网络对齐器具有更高的灵敏度和特异性。Graemlin 2.0在GNU公共许可下可在http://graemlin.stanford.edu获得。
We developed Graemlin 2.0, a new multiple network aligner with ( 1) a new multi-stage approach to local network alignment; ( 2) a novel scoring function that can use arbitrary features of a multiple network alignment, such as protein deletions, protein duplications, protein mutations, and interaction losses; ( 3) a parameter learning algorithm that uses a training set of known network alignments to learn parameters for our scoring function and thereby adapt it to any set of networks; and ( 4) an algorithm that uses our scoring function to find approximate multiple network alignments in linear time. We tested Graemlin 2.0's accuracy on protein interaction networks from IntAct, DIP, and the Stanford Network Database. We show that, on each of these datasets, Graemlin 2.0 has higher sensitivity and specificity than existing network aligners. Graemlin 2.0 is available under the GNU public license at http://graemlin.stanford.edu.