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              多層異構生物網絡候選疾病基因識別

              丁蒼峰 王君 張紫蕓

              丁蒼峰, 王君, 張紫蕓. 多層異構生物網絡候選疾病基因識別. 自動化學報, 2024, 50(6): 1?15 doi: 10.16383/j.aas.c210577
              引用本文: 丁蒼峰, 王君, 張紫蕓. 多層異構生物網絡候選疾病基因識別. 自動化學報, 2024, 50(6): 1?15 doi: 10.16383/j.aas.c210577
              Ding Cang-Feng, Wang Jun, Zhang Zi-Yun. Identifying candidate disease genes in multilayer heterogeneous networks. Acta Automatica Sinica, 2024, 50(6): 1?15 doi: 10.16383/j.aas.c210577
              Citation: Ding Cang-Feng, Wang Jun, Zhang Zi-Yun. Identifying candidate disease genes in multilayer heterogeneous networks. Acta Automatica Sinica, 2024, 50(6): 1?15 doi: 10.16383/j.aas.c210577

              多層異構生物網絡候選疾病基因識別

              doi: 10.16383/j.aas.c210577
              基金項目: 國家自然科學基金(62041212, 61866038, 61763046, 61962059), 陜西省自然科學基礎研究計劃(2020JM-548, 2020JM-547), 延安大學基金(YDZ2019-04, YDBK2018-35)資助
              詳細信息
                作者簡介:

                丁蒼峰:延安大學數學與計算機科學學院副教授. 2018年獲北京理工大學博士學位. 主要研究方向為多層復雜網絡, 圖神經網絡和自然語言處理. 本文通信作者. E-mail: dcf@yau.edu.cn

                王君:延安大學數學與計算機科學學院碩士研究生. 主要研究方向為知識圖譜及其在紅色文化、文學小說等領域相關應用. E-mail: wangjun03006@163.com

                張紫蕓:延安大學數學與計算機科學學院碩士研究生. 主要研究方向為文本摘要及其在紅色文本等領域相關應用. E-mail: zhangziyun1202@163.com

              Identifying Candidate Disease Genes in Multilayer Heterogeneous Networks

              Funds: Supported by National Natural Science Foundation of China (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)
              More Information
                Author Bio:

                DING Cang-Feng Associate professor at the School of Mathematics and Computer Science, Yan'an University. He received his Ph.D. degree from Beijing Institute of Technology in 2018. His research interest covers multilayer complex network, graph neural network, and natural language processing. Corresponding author of this paper

                WANG Jun Master student at the School of Mathematics and Computer Science, Yan'an University. His research interest covers knowledge graph and its related applications in the field of red culture and literary novels

                ZHANG Zi-Yun Master student at the School of Mathematics and Computer Science, Yan'an University. Her research interest covers text summarization and its related applications in the field of red text

              • 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先訪問高度連接的基因, 而可能與已知疾病有關的不知名或連接性差的基因易被忽略或難以識別. 此外, 這些方法僅訪問單個基因網絡或各種基因數據的聚合網絡, 導致偏差和不完整性. 因此, 設計一種能控制隨機游走運動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迫切需要解決的問題. 為此, 首先構建多層網絡和多層異構基因網絡. 然后, 提出一種游走于多層網絡和多層異構網絡的拓撲偏置重啟隨機游走(Biased random walk with restart, BRWR)算法來識別疾病基因. 實驗結果表明, 游走于不同類型網絡上的識別候選疾病基因的BRWR算法優于現有的算法. 最后, 應用于多層異構網絡上的BRWR算法能預測未診斷的新生兒類早衰綜合征中涉及的疾病基因.
                1)  21 http://www.proteinatlas.org2 http://www.biocarta.com
                2)  1http://www.biocarta.com
                3)  33 http://human-phenotype-ontology.github.io/4 http://www.omim.org/
                4)  4http://www.omim.org/
                5)  55 https://www.ncbi.nlm.nih.gov/geo/
              • 圖  1  多層網絡、異構網絡、多層異構網絡以及探索它們的隨機游走路徑(箭頭的實線)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  非異構基因網絡上不同方法的ROC曲線及其對應的AUC值

                Fig.  2  ROC curves and AUC values of different algorithms on the non-heterogeneous gene networks

                圖  3  異構基因網絡上不同方法的ROC曲線及其對應的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 $變數的累積分布

                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時的網絡表示[51]

                Fig.  6  Network representation when all the biased parameters equal to 5[51]

                圖  7  所有偏置參數為?5時的網絡表示[51]

                Fig.  7  Network representation when all the biased parameters equal to ?5[51]

                圖  8  所有偏置參數為?1時的網絡表示[51]

                Fig.  8  Network representation when all the biased parameters equal to ?1[51]

                圖  9  所有偏置參數為0時的網絡表示[51]

                Fig.  9  Network representation when all the biased parameters equal to 0[51]

                圖  10  所有偏置參數為1時的網絡表示[51]

                Fig.  10  Network representation when all the biased parameters equal to 1[51]

                表  1  表型、基因和聚合網絡的統計屬性

                Table  1  Statistical properties of phenotype, gene and aggregated networks

                網絡節點數邊數平均度
                COEX10 415998 71247.44
                PPI12 89370 1417.73
                PATH10 966274 05113.47
                聚合網絡17 6111 342 70325.79
                表型網絡7 32429 8534.38
                下載: 導出CSV

                表  2  不同的非異構網絡上的不同方法的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  不同異構網絡上的不同方法的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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                        • 收稿日期:  2021-06-25
                        • 錄用日期:  2022-02-10
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