Identifying Candidate Disease Genes in Multilayer Heterogeneous Biological Networks
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摘要: 现有大多数用于识别候选疾病基因的随机游走方法通常优先访问高度连接的基因, 而可能与已知疾病有关的不知名或连接性差的基因易被忽略或难以识别. 此外, 这些方法仅访问单个基因网络或各种基因数据的聚合网络, 导致偏差和不完整性. 因此, 设计一种能控制随机游走运动方向和整合多种数据源的候选疾病基因识别方法将是一个迫切需要解决的问题. 为此, 首先构建多层网络和多层异构基因网络. 然后, 提出一种游走于多层网络和多层异构网络的拓扑偏置重启随机游走(Biased random walk with restart, BRWR)算法来识别疾病基因. 实验结果表明, 游走于不同类型网络上的识别候选疾病基因的BRWR算法优于现有的算法. 最后, 应用于多层异构网络上的BRWR算法能预测未诊断的新生儿类早衰综合征中涉及的疾病基因.Abstract: Most of existing random walk methods to identify candidate disease genes preferentially visit highly-connected genes, while unwell-known or poorly-connected genes probably relevant to known diseases are more easily ignored or complicated to identify. Moreover, these methods access only a single gene network or an aggregated network of various gene data, leading to bias and incompleteness. Therefore, it is a pressing challenge for controlling the motion direction of random walk and for integrating multiple data sources involving different information for disease-gene identification. To this end, we first construct a multilayer network and multilayer heterogeneous genetic network. Then, we propose a topologically biased random walk with restart (BRWR) algorithm applicable to multilayer and multilayer heterogeneous networks for the identification of candidate disease genes. Experimental results show that the BRWR algorithm to identify candidate disease genes outperforms the state-of-the-art ones on different types of networks. Finally, the BRWR algorithm on multilayer heterogeneous networks is used to predict disease genes implicated in the undiagnosed neonatal progeroid syndrome.1)
2 1http://www.proteinatlas.org 2http://www.biocarta.com 2)1 http://www.biocarta.com3)3 3http://human-phenotype-ontology.github.io/ 4http://www.omim.org/ 4)4 http://www.omim.org/5)5 5 https://www.ncbi.nlm.nih.gov/geo/ -
图 1 多层网络、异构网络、多层异构网络以及探索它们的随机游走路径(箭头的实线)的示意图
Fig. 1 Schematic of multilayer, heterogeneous and multilayer heterogeneous networks, together with paths of random walks (arrow solid lines)
图 2 非异构基因网络上不同方法的ROC曲线及其对应的AUC值
Fig. 2 ROC curves and AUC values of different algorithms on the non-heterogeneous gene networks
图 3 异构基因网络上不同方法的ROC曲线及其对应的AUC值
Fig. 3 ROC curves and AUC values of different algorithms on the heterogeneous gene networks
图 4 排名随偏置参数$ b $变化的累积分布
Fig. 4 The cumulative distributions of the ranking with change of the biased parameter $ b $
图 5 排名随参数变化的累积分布
Fig. 5 The cumulative distributions of the ranking with change of the parameters
图 7 所有偏置参数为 −5时的网络表示
Fig. 7 Network representation when all the biased parameters are −5
图 8 所有偏置参数为 −1时的网络表示
Fig. 8 Network representation when all the biased parameters are −1
图 10 所有偏置参数为1时的网络表示
Fig. 10 Network representation when all the biased parameters are 1
表 1 表型、基因和聚合网络的统计属性
Table 1 Statistical properties of phenotype, gene and aggregated networks
网络 节点数 边数 平均度 COEX 10 415 998 712 47.44 PPI 12 893 70 141 7.73 PATH 10 966 274 051 13.47 聚合网络 17 611 1 342 703 25.79 表型网络 7 324 29 853 4.38 
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表 2 不同的非异构网络上的不同方法的AUC值(%)
Table 2 AUC values of different algorithms on different non-heterogeneous networks (%)
PPI COEX PATH Aggregated Multilayer RWR 73.35 72.84 74.43 76.53 77.98 ProDige 79.12 73.63 80.29 83.27 84.12 NDOS 78.27 74.78 79.86 84.49 87.95 DRS 78.93 74.94 80.87 84.78 88.45 BRIDGE 79.91 74.26 81.51 85.13 89.33 BRWR 81.15 75.20 84.18 86.73 90.17
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表 3 不同异构网络上的不同方法的AUC值(%)
Table 3 AUC values of different algorithms on different heterogeneous networks (%)
PPIH COEXH PATHH AggregatedH MultilayerH CIPHER 74.52 73.51 78.30 77.89 78.31 RWRH 80.37 75.34 79.47 83.67 86.53 MAXIF 80.91 76.56 80.15 84.02 88.43 LapRWRH 81.91 77.80 80.90 84.93 88.78 NRWRH 81.36 78.38 82.70 86.56 89.36 IDLP 82.08 79.25 83.37 87.79 90.16 BRWRH 82.36 80.91 85.17 89.65 91.09
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