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Protein Function Prediction Based on Active Semi-supervised Learning

机译：基于主动半监督学习的蛋白质功能预测

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摘要

In our study, the active learning and semi-supervised learning methods are comprehensively used for label delivery of proteins with known functions in Protein-protein interaction (PPI) network so as to predict the functions of unknown proteins. Because the real PPI network is generally observed with overlapping protein nodes with multiple functions, the mislabeling of overlapping protein may result in accumulation of prediction errors. For this reason, prior to executing the label delivery process of semi-supervised learning, the adjacency matrix is used to detect overlapping proteins. As the topological structure description of interactive relation between proteins, PPI network is observed with party hub protein nodes that play an important role, in co-expression with its neighborhood. Therefore, to reduce the manual labeling cost, party hub proteins most beneficial for improvement of prediction accuracy are selected for class labeling and the labeled party hub proteins are added into the labeled sample set for semi-supervised learning later. As the experimental results of real yeast PPI network show, the proposed algorithm can achieve high prediction accuracy with few labeled samples.
机译：在我们的研究中，主动学习和半监督学习方法被广泛用于在蛋白质-蛋白质相互作用（PPI）网络中具有已知功能的蛋白质的标签递送，从而预测未知蛋白质的功能。由于通常会在具有多个功能的重叠蛋白节点处观察到真实的PPI网络，因此重叠蛋白的错误标签可能会导致预测误差的累积。因此，在执行半监督学习的标签传递过程之前，将邻接矩阵用于检测重叠蛋白。作为蛋白质之间相互作用关系的拓扑结构描述，观察到PPI网络及其在其邻域共表达中起重要作用的当事人中心蛋白质节点。因此，为了减少人工标记成本，选择最有利于提高预测精度的方集线器蛋白进行类标记，然后将标记的方集线器蛋白添加到标记的样本集中以供以后进行半监督学习。如真实酵母PPI网络的实验结果所示，该算法可以在较少的标记样本的情况下达到较高的预测精度。

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