Predicting Protein Function by Multi-Label Correlated Semi-Supervised Learning
Predicting Protein Function by Multi-Label Correlated Semi-Supervised Learning
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DOI：
10.1109/tcbb.2011.156
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发表时间：
2012-07-01
影响因子：
4.5
通讯作者：
McQuay, Lisa J.
中科院分区：
文献类型：
--
作者：
Jiang, Jonathan Q.;McQuay, Lisa J.
将生物学功能转化为未知的蛋白质是后基因组时代的一个基本问题。蛋白质-蛋白质相互作用（PPI）的大量数据的日益可用性导致了在网络背景下用于确定蛋白质功能的大量计算方法的出现。然而，这些算法孤立地对待每个函数类，因此经常面临标记数据稀缺的困难。实际上，不同的函数类自然是相互依赖的。我们提出了一种新的算法，多标签相关的半监督学习（MCSL），通过利用PPI网络和功能类网络提供的关系，将功能类之间的内在相关性纳入蛋白质功能预测。指导性的直觉是，分类函数在子图上应该足够平滑，其中这两个网络的各自拓扑结构是良好的匹配。我们将这种直觉编码为具有类内和类间一致性的正则化学习，这可以理解为基于局部和全局一致性的图学习（LGC）方法的扩展。酵母蛋白质组的交叉验证表明，MCSL始终优于几个国家的最先进的方法。最值得注意的是，它有效地克服了与标签数据稀缺相关的问题。补充文件可在www.example.com上免费获得。
Assigning biological functions to uncharacterized proteins is a fundamental problem in the postgenomic era. The increasing availability of large amounts of data on protein-protein interactions (PPIs) has led to the emergence of a considerable number of computational methods for determining protein function in the context of a network. These algorithms, however, treat each functional class in isolation and thereby often suffer from the difficulty of the scarcity of labeled data. In reality, different functional classes are naturally dependent on one another. We propose a new algorithm, Multi-label Correlated Semi-supervised Learning (MCSL), to incorporate the intrinsic correlations among functional classes into protein function prediction by leveraging the relationships provided by the PPI network and the functional class network. The guiding intuition is that the classification function should be sufficiently smooth on subgraphs where the respective topologies of these two networks are a good match. We encode this intuition as regularized learning with intraclass and interclass consistency, which can be understood as an extension of the graph-based learning with local and global consistency (LGC) method. Cross validation on the yeast proteome illustrates that MCSL consistently outperforms several state-of-the-art methods. Most notably, it effectively overcomes the problem associated with scarcity of label data. The supplementary files are freely available at http://sites.google.com/site/csaijiang/MCSL.