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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >SEQUENTIAL LINEAR NEIGHBORHOOD PROPAGATION FOR SEMI-SUPERVISED PROTEIN FUNCTION PREDICTION
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SEQUENTIAL LINEAR NEIGHBORHOOD PROPAGATION FOR SEMI-SUPERVISED PROTEIN FUNCTION PREDICTION

机译：序列线性近邻传播用于半监督蛋白质功能预测

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摘要

Predicting protein function is one of the most challenging problems of the post-genomic era. The development of experimental methods for genome scale analysis of molecular interaction networks has provided new approaches to inferring protein function. In this paper we introduce a new graph-based semi-supervised classification algorithm Sequential Linear Neighborhood Propagation (SLNP), which addresses the problem of the classification of partially labeled protein interaction networks. The proposed SLNP first constructs a sequence of node sets according to their shortest distance to the labeled nodes, and then predicts the function of the unlabel proteins from the set closer to labeled one, using Linear Neighborhood Propagation. Its performance is assessed on the Saccharomyces cerevisiae PPI network data sets, with good results compared with three current state-of-the-art algorithms, especially in settings where only a small fraction of the proteins are labeled.
机译：预测蛋白质功能是后基因组时代最具挑战性的问题之一。用于分子相互作用网络的基因组规模分析的实验方法的发展提供了推断蛋白质功能的新方法。在本文中，我们介绍了一种新的基于图的半监督分类算法顺序线性邻域传播（SLNP），该算法解决了部分标记的蛋白质相互作用网络的分类问题。提出的SLNP首先根据节点集到标记节点的最短距离构建一个节点集序列，然后使用线性邻域传播从更接近标记集的集合预测未标记蛋白的功能。在啤酒酵母PPI网络数据集上评估了它的性能，与三种最新的算法相比，具有良好的结果，尤其是在只有一小部分蛋白质被标记的环境中。

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