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首页> 外文会议>International joint conference on artificial intelligence >Protein Function Prediction via Laplacian Network Partitioning Incorporating Function Category Correlations
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Protein Function Prediction via Laplacian Network Partitioning Incorporating Function Category Correlations

机译：通过结合功能类别相关性的Laplacian网络分区进行蛋白质功能预测

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摘要

Understanding the molecular mechanisms of life requires decoding the functions of the proteins in an organism.Various high-throughput experimental techniques have been developed to characterize biological systems at the genome scale.A fundamental challenge of the post-genomic era is to assign biological functions to all the proteins encoded by the genome using high-throughput biological data.To address this challenge,we propose a novel Laplacian Network Partitioning incorporating function category Correlations (LNPC) method to predict protein function on proteinprotein interaction (PPI) networks by optimizing a Laplacian based quotient objective function that seeks the optimal network configuration to maximize consistent function assignments over edges on the whole graph.Unlike the existing approaches that have no unique optimization solutions,our optimization problem has unique global solution by eigen-decomposition methods.The correlations among protein function categories are quantified and incorporated into a correlated protein affinity graph which is integrated into the PPI graph to significantly improve the protein function prediction accuracy.We apply our new method to the BioGRID dataset for the Saccharomyces Cerevisiae species using the MIPS annotation scheme.Our new method outperforms other related state-of-the-art approaches more than 63％ by the average precision of function prediction and 53％ by the average F1 score.
机译：了解生命的分子机制需要解码生物体中蛋白质的功能。已开发出各种高通量实验技术来表征基因组规模的生物系统。后基因组时代的一项基本挑战是将生物学功能分配给为了解决这一挑战，我们提出了一种新颖的拉普拉斯网络划分方法，该模型结合了功能类别相关性（LNPC）方法，通过优化基于Laplacian的蛋白质-蛋白质相互作用（PPI）网络来预测蛋白质功能寻求最佳网络配置以最大化整个图边缘上一致的功能分配的商目标函数。与不存在唯一优化解决方案的现有方法不同，我们的优化问题通过特征分解方法具有唯一的全局解决方案。蛋白质功能之间的相关性类别是数量并整合到相关的蛋白质亲和图中，该蛋白质亲和图中已整合到PPI图中，以显着提高蛋白质功能的预测准确性。功能预测的平均精度接近63％，而F1平均得分则达到53％。

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  • 会议地点 Beijing(CN)
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    Hua Wang; Heng Huang; Chris Ding;

  • 作者单位

    Department of Electrical Engineering and Computer Science Colorado School of Mines Golden Colorado 80401 USA;

    Department of Computer Science and Engineering University of Texas at Arlington Arlington Texas 76019 USA;

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