{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:13:19Z","timestamp":1750219999785,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T00:00:00Z","timestamp":1666915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Shanghai Municipal Science and Technology Major Project","award":["No.2018SHZDZX01"],"award-info":[{"award-number":["No.2018SHZDZX01"]}]},{"name":"the National Natural Science Foundation of China","award":["62162019, 62166014, 11961015"],"award-info":[{"award-number":["62162019, 62166014, 11961015"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,28]]},"DOI":"10.1145\/3571532.3571540","type":"proceedings-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T23:10:33Z","timestamp":1675725033000},"page":"59-65","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Microbe-Disease Associations via Multiple Layer Graph Convolutional Network and Attention Mechanism"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0038-625X","authenticated-orcid":false,"given":"Kai","family":"Shi","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Guilin University of Technology, China and Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6934-4504","authenticated-orcid":false,"given":"Lin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Guilin University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1661-0503","authenticated-orcid":false,"given":"Juehua","family":"Yu","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Kunming Medical University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6167-7945","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Guilin University of Technolog, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9546-5115","authenticated-orcid":false,"given":"Xiaolan","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Guilin University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,2,6]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1177\/0022034520924633"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105997"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1238-8"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbw005"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btw715"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2019.00291"},{"issue":"6","key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","first-page":"2502","DOI":"10.1109\/TCBB.2020.2986459","article-title":"Identifying Microbe-Disease Association Based on a Novel Back-Propagation Neural Network Model","volume":"18","author":"Hao Li","year":"2020","unstructured":"Li Hao , Wang Yuqi , Zhang Zhen , Tan Yihong , Chen Zhiqing , Wang Xiangyi , Pei Tingrui , and Wang Lei . 2020 . Identifying Microbe-Disease Association Based on a Novel Back-Propagation Neural Network Model . IEEE\/ACM Trans Comput Biol Bioinform. 18 ( 6 ): 2502 - 2513 . DOI: https:\/\/doi.org\/10.1109\/TCBB.2020.2986459. 10.1109\/TCBB.2020.2986459 Li Hao, Wang Yuqi, Zhang Zhen, Tan Yihong, Chen Zhiqing, Wang Xiangyi, Pei Tingrui, and Wang Lei. 2020. Identifying Microbe-Disease Association Based on a Novel Back-Propagation Neural Network Model. IEEE\/ACM Trans Comput Biol Bioinform. 18(6):2502-2513. DOI: https:\/\/doi.org\/10.1109\/TCBB.2020.2986459.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform."},{"key":"#cr-split#-e_1_3_2_1_8_1.1","doi-asserted-by":"crossref","unstructured":"Long Yahui Luo Jiawei Zhang Yu and Xia Yan. 2021. Predicting human microbe-disease associations via graph attention networks with inductive matrix completion. Briefings in Bioinformatics. 22(3):bbaa146. DOI: https:\/\/doi.org\/10.1093\/bib\/bbaa146. 10.1093\/bib","DOI":"10.1093\/bib\/bbaa146"},{"key":"#cr-split#-e_1_3_2_1_8_1.2","doi-asserted-by":"crossref","unstructured":"Long Yahui Luo Jiawei Zhang Yu and Xia Yan. 2021. Predicting human microbe-disease associations via graph attention networks with inductive matrix completion. Briefings in Bioinformatics. 22(3):bbaa146. DOI: https:\/\/doi.org\/10.1093\/bib\/bbaa146.","DOI":"10.1093\/bib\/bbaa146"},{"key":"e_1_3_2_1_9_1","first-page":"209","article-title":"MASI: microbiota-active substance interactions database","volume":"15","author":"Xian Zeng","year":"2021","unstructured":"Zeng Xian , Yang Xue , Fan Jiajun , Tan Ying , Ju Lingyi , Shen Wanxiang , Wang Yali , Wang Xinghao , Chen Weiping , Ju Dianwen , and Chen YuZong . 2021 . MASI: microbiota-active substance interactions database . Nucleic Acids Res. 15 : 209 . DOI: https:\/\/doi.org\/10.1093\/nar\/gkaa924. 10.1093\/nar Zeng Xian, Yang Xue, Fan Jiajun, Tan Ying, Ju Lingyi, Shen Wanxiang, Wang Yali, Wang Xinghao, Chen Weiping, Ju Dianwen, and Chen YuZong. 2021. MASI: microbiota-active substance interactions database. Nucleic Acids Res. 15:209. DOI: https:\/\/doi.org\/10.1093\/nar\/gkaa924.","journal-title":"Nucleic Acids Res."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbz057"},{"key":"e_1_3_2_1_11_1","volume-title":"Yang Sunmo, Kim Eiru, Hart Traver, Edward M Marcotte, and Lee Insuk","author":"Sohyun Hwang","year":"2019","unstructured":"Hwang Sohyun , Kim Chan Yeong , Yang Sunmo, Kim Eiru, Hart Traver, Edward M Marcotte, and Lee Insuk 2019 . HumanNet v2: human gene networks for disease research. Nucleic Acids Res. 47(D1):D573-D580. DOI: https:\/\/doi.org\/10.1093\/nar\/gky1126. 10.1093\/nar Hwang Sohyun, Kim Chan Yeong, Yang Sunmo, Kim Eiru, Hart Traver, Edward M Marcotte, and Lee Insuk 2019. HumanNet v2: human gene networks for disease research. Nucleic Acids Res. 47(D1):D573-D580. DOI: https:\/\/doi.org\/10.1093\/nar\/gky1126."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005366"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymeth.2021.10.008"},{"key":"#cr-split#-e_1_3_2_1_14_1.1","doi-asserted-by":"crossref","unstructured":"Yu Zhouxin Huang Feng Zhao Xiaohan Xiao Wenjie and Zhang Wen. 2021. Predicting drug-disease associations through layer attention graph convolutional network. Brief Bioinform. 22(4):bbaa243. DOI: https:\/\/doi.org\/10.1093\/bib\/bbaa243. 10.1093\/bib","DOI":"10.1093\/bib\/bbaa243"},{"key":"#cr-split#-e_1_3_2_1_14_1.2","doi-asserted-by":"crossref","unstructured":"Yu Zhouxin Huang Feng Zhao Xiaohan Xiao Wenjie and Zhang Wen. 2021. Predicting drug-disease associations through layer attention graph convolutional network. Brief Bioinform. 22(4):bbaa243. DOI: https:\/\/doi.org\/10.1093\/bib\/bbaa243.","DOI":"10.1093\/bib\/bbaa243"},{"key":"#cr-split#-e_1_3_2_1_15_1.1","doi-asserted-by":"crossref","unstructured":"Du Zhi-Hua Wu Yang-Han Huang Yu-An Chen Jie Pan Gui-Qing Hu Lun You Zhu-Hong and Li Jian-Qiang. 2022. GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions. Brief Bioinform. 23(3):bbac148. DOI: https:\/\/doi.org\/10.1093\/bib\/bbac148. 10.1093\/bib","DOI":"10.1093\/bib\/bbac148"},{"key":"#cr-split#-e_1_3_2_1_15_1.2","doi-asserted-by":"crossref","unstructured":"Du Zhi-Hua Wu Yang-Han Huang Yu-An Chen Jie Pan Gui-Qing Hu Lun You Zhu-Hong and Li Jian-Qiang. 2022. GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions. Brief Bioinform. 23(3):bbac148. DOI: https:\/\/doi.org\/10.1093\/bib\/bbac148.","DOI":"10.1093\/bib\/bbac148"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1007\/978-3-319-95957-3_78","volume-title":"Intelligent Computing Methodologies.","author":"Xianjun Shen","year":"2018","unstructured":"Shen Xianjun , Zhu Huan , Jiang Xingpeng , Hu Xiaohua , and Yang Jincai , A Novel Approach Based on Bi-Random Walk to Predict Microbe-Disease Associations , in Intelligent Computing Methodologies. 2018 . p. 746 - 752 . Shen Xianjun, Zhu Huan, Jiang Xingpeng, Hu Xiaohua, and Yang Jincai, A Novel Approach Based on Bi-Random Walk to Predict Microbe-Disease Associations, in Intelligent Computing Methodologies. 2018. p. 746-752."},{"issue":"5","key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.1109\/TCBB.2019.2907626","article-title":"BRWMDA:Predicting Microbe-Disease Associations Based on Similarities and Bi-Random Walk on Disease and Microbe Networks","volume":"17","author":"Cheng Yan","year":"2020","unstructured":"Yan Cheng , Duan Guihua , Wu Fangxiang , Pan Yi , and Wang Jianxin . 2020 . BRWMDA:Predicting Microbe-Disease Associations Based on Similarities and Bi-Random Walk on Disease and Microbe Networks . IEEE\/ACM Trans Comput Biol Bioinform. 17 ( 5 ): 1595 - 1604 . DOI: https:\/\/doi.org\/10.1109\/tcbb.2019.2907626. 10.1109\/tcbb.2019.2907626 Yan Cheng, Duan Guihua, Wu Fangxiang, Pan Yi, and Wang Jianxin. 2020. BRWMDA:Predicting Microbe-Disease Associations Based on Similarities and Bi-Random Walk on Disease and Microbe Networks. IEEE\/ACM Trans Comput Biol Bioinform. 17(5):1595-1604. DOI: https:\/\/doi.org\/10.1109\/tcbb.2019.2907626.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform."},{"issue":"4","key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TCBB.2018.2883041","article-title":"NTSHMDA: Prediction of Human Microbe-Disease Association Based on Random Walk by Integrating Network Topological Similarity","volume":"17","author":"Jiawei Luo","year":"2020","unstructured":"Luo Jiawei and Long Yahui . 2020 . NTSHMDA: Prediction of Human Microbe-Disease Association Based on Random Walk by Integrating Network Topological Similarity . IEEE\/ACM Trans Comput Biol Bioinform. 17 ( 4 ): 1341 - 1351 . DOI: https:\/\/doi.org\/10.1109\/tcbb.2018.2883041. 10.1109\/tcbb.2018.2883041 Luo Jiawei and Long Yahui. 2020. NTSHMDA: Prediction of Human Microbe-Disease Association Based on Random Walk by Integrating Network Topological Similarity. IEEE\/ACM Trans Comput Biol Bioinform. 17(4):1341-1351. DOI: https:\/\/doi.org\/10.1109\/tcbb.2018.2883041.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform."},{"issue":"1","key":"e_1_3_2_1_19_1","first-page":"1","article-title":"LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe-Disease Association prediction","volume":"7","author":"Fan Wang","year":"2017","unstructured":"Wang Fan , Huang Zhi-An , Chen Xing , Zhu Zexuan , Wen Zhenkun , Zhao Jiyun , and Yan GuiYing . 2017 . LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe-Disease Association prediction . Sci Rep. 7 ( 1 ): 1 - 11 . DOI: https:\/\/doi.org\/10.1038\/s41598-017-08127-2. 10.1038\/s41598-017-08127-2 Wang Fan, Huang Zhi-An, Chen Xing, Zhu Zexuan, Wen Zhenkun, Zhao Jiyun , and Yan GuiYing. 2017. LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe-Disease Association prediction. Sci Rep. 7(1):1-11. DOI: https:\/\/doi.org\/10.1038\/s41598-017-08127-2.","journal-title":"Sci Rep."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2019.00684"},{"key":"e_1_3_2_1_21_1","first-page":"5079","article-title":"NCPLP: A Novel Approach for Predicting Microbe-Associated Diseases With Network Consistency Projection and Label Propagation","volume":"52","author":"Meng-Meng Yin","year":"2020","unstructured":"Yin Meng-Meng , Liu Jin-X Ing , Gao Ying-Lian , Kong Xiang-Zhen , and Zheng Chun-Hou . 2020 . NCPLP: A Novel Approach for Predicting Microbe-Associated Diseases With Network Consistency Projection and Label Propagation . IEEE Trans Cybern. 52 : 5079 - 5087 . DOI: https:\/\/doi.org\/10.1109\/TCYB.2020.3026652. 10.1109\/TCYB.2020.3026652 Yin Meng-Meng, Liu Jin-XIng, Gao Ying-Lian, Kong Xiang-Zhen, and Zheng Chun-Hou. 2020. NCPLP: A Novel Approach for Predicting Microbe-Associated Diseases With Network Consistency Projection and Label Propagation. IEEE Trans Cybern. 52:5079-5087. DOI: https:\/\/doi.org\/10.1109\/TCYB.2020.3026652.","journal-title":"IEEE Trans Cybern."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.3390\/nano12010169"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12866-022-02465-6"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.12938\/bmfh.2020-010"},{"key":"e_1_3_2_1_25_1","first-page":"3140070","article-title":"Correlations between Intestinal Microbiota and Clinical Characteristics in Colorectal Adenoma\/Carcinoma","volume":"2022","author":"Caizhao Lin","year":"2022","unstructured":"Lin Caizhao , Li Baolong , Tu Chunyi , Chen Xiaohua , and Guo Min . 2022 . Correlations between Intestinal Microbiota and Clinical Characteristics in Colorectal Adenoma\/Carcinoma . Biomed Research International. 2022 : 3140070 . DOI: https:\/\/doi.org\/10.1155\/2022\/3140070. 10.1155\/2022 Lin Caizhao, Li Baolong, Tu Chunyi, Chen Xiaohua, and Guo Min. 2022. Correlations between Intestinal Microbiota and Clinical Characteristics in Colorectal Adenoma\/Carcinoma. Biomed Research International. 2022:3140070. DOI: https:\/\/doi.org\/10.1155\/2022\/3140070.","journal-title":"Biomed Research International."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(21)01374-X"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms22010199"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.3390\/nu12061874"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.982128"}],"event":{"name":"ICBBS 2022: 2022 11th International Conference on Bioinformatics and Biomedical Science","acronym":"ICBBS 2022","location":"Nanning China"},"container-title":["Proceedings of the 2022 11th International Conference on Bioinformatics and Biomedical Science"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3571532.3571540","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3571532.3571540","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:50:55Z","timestamp":1750182655000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3571532.3571540"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,28]]},"references-count":32,"alternative-id":["10.1145\/3571532.3571540","10.1145\/3571532"],"URL":"https:\/\/doi.org\/10.1145\/3571532.3571540","relation":{},"subject":[],"published":{"date-parts":[[2022,10,28]]},"assertion":[{"value":"2023-02-06","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}