多層異構生物網(wǎng)絡(luò )候選疾病基因識別
doi: 10.16383/j.aas.c210577
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延安大學(xué)數學(xué)與計算機科學(xué)學(xué)院 延安 716000
基金項目: 國家自然科學(xué)基金(62262067, 62041212, 61866038, 61763046, 61962059), 陜西省自然科學(xué)基礎研究計劃(2020JM-548, 2020JM-547), 延安大學(xué)基金(YDZ2019-04, YDBK2018-35)資助
Identifying Candidate Disease Genes in Multilayer Heterogeneous Biological Networks
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College of Mathematics and Computer Science, Yan＇an University, Yan＇an 716000
Funds: Supported by National Natural Science Foundation of China (62262067, 62041212, 61866038, 61763046, 61962059), Natural Science Basic Research Program of Shaanxi (2020JM-548, 2020JM-547), and Yan＇an University Foundation Program (YDZ2019-04, YDBK2018-35)
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摘要: 現有大多數用于識別候選疾病基因的隨機游走方法通常優(yōu)先訪(fǎng)問(wèn)高度連接的基因, 而可能與已知疾病有關(guān)的不知名或連接性差的基因易被忽略或難以識別. 此外, 這些方法僅訪(fǎng)問(wèn)單個(gè)基因網(wǎng)絡(luò )或各種基因數據的聚合網(wǎng)絡(luò ), 導致偏差和不完整性. 因此, 設計一種能控制隨機游走運動(dòng)方向和整合多種數據源的候選疾病基因識別方法將是一個(gè)迫切需要解決的問(wèn)題. 為此, 首先構建多層網(wǎng)絡(luò )和多層異構基因網(wǎng)絡(luò ). 然后, 提出一種游走于多層網(wǎng)絡(luò )和多層異構網(wǎng)絡(luò )的拓撲偏置重啟隨機游走(Biased random walk with restart, BRWR)算法來(lái)識別疾病基因. 實(shí)驗結果表明, 游走于不同類(lèi)型網(wǎng)絡(luò )上的識別候選疾病基因的BRWR算法優(yōu)于現有的算法. 最后, 應用于多層異構網(wǎng)絡(luò )上的BRWR算法能預測未診斷的新生兒類(lèi)早衰綜合征中涉及的疾病基因.
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關(guān)鍵詞:
- 多層異構網(wǎng)絡(luò ) /
- 生物網(wǎng)絡(luò ) /
- 偏置隨機游走 /
- 候選基因識別
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/ -
圖 1 多層網(wǎng)絡(luò )、異構網(wǎng)絡(luò )、多層異構網(wǎng)絡(luò )以及探索它們的隨機游走路徑(箭頭的實(shí)線(xiàn))的示意圖
Fig. 1 Schematic of multilayer, heterogeneous and multilayer heterogeneous networks, together with paths of random walks (arrow solid lines)
圖 2 非異構基因網(wǎng)絡(luò )上不同方法的ROC曲線(xiàn)及其對應的AUC值
Fig. 2 ROC curves and AUC values of different algorithms on the non-heterogeneous gene networks
圖 3 異構基因網(wǎng)絡(luò )上不同方法的ROC曲線(xiàn)及其對應的AUC值
Fig. 3 ROC curves and AUC values of different algorithms on the heterogeneous gene networks
圖 4 排名隨偏置參數$ b $變化的累積分布
Fig. 4 The cumulative distributions of the ranking with change of the biased parameter $ b $
圖 5 排名隨參數變化的累積分布
Fig. 5 The cumulative distributions of the ranking with change of the parameters
圖 6 所有偏置參數為5時(shí)的網(wǎng)絡(luò )表示
Fig. 6 Network representation when all the biased parameters are 5
圖 7 所有偏置參數為 ?5時(shí)的網(wǎng)絡(luò )表示
Fig. 7 Network representation when all the biased parameters are ?5
圖 8 所有偏置參數為 ?1時(shí)的網(wǎng)絡(luò )表示
Fig. 8 Network representation when all the biased parameters are ?1
圖 9 所有偏置參數為0時(shí)的網(wǎng)絡(luò )表示
Fig. 9 Network representation when all the biased parameters are 0
圖 10 所有偏置參數為1時(shí)的網(wǎng)絡(luò )表示
Fig. 10 Network representation when all the biased parameters are 1
表 1 表型、基因和聚合網(wǎng)絡(luò )的統計屬性
Table 1 Statistical properties of phenotype, gene and aggregated networks
網(wǎng)絡(luò ) 節點(diǎn)數 邊數 平均度 COEX 10 415 998 712 47.44 PPI 12 893 70 141 7.73 PATH 10 966 274 051 13.47 聚合網(wǎng)絡(luò ) 17 611 1 342 703 25.79 表型網(wǎng)絡(luò ) 7 324 29 853 4.38 
下載: 導出CSV
表 2 不同的非異構網(wǎng)絡(luò )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
下載: 導出CSV
表 3 不同異構網(wǎng)絡(luò )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
下載: 導出CSV
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