{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:59:00Z","timestamp":1785340740554,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"College of Sciences, Old Dominion University"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,30]]},"DOI":"10.1145\/3807503.3819442","type":"proceedings-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T02:55:27Z","timestamp":1785293727000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AttF-GNN: An Attention-Based Multi-omics Graph Neural Network with Modality Learning for Disease Subtyping"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0765-1060","authenticated-orcid":false,"given":"Sovon","family":"Chakraborty","sequence":"first","affiliation":[{"name":"Computer Science, Old Dominion University, Norfolk, Virginina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7548-4375","authenticated-orcid":false,"given":"Eleni","family":"Adam","sequence":"additional","affiliation":[{"name":"Computer Science, Old Dominion University, Norfolk, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1179-4606","authenticated-orcid":false,"given":"Terry","family":"Stilwell","sequence":"additional","affiliation":[{"name":"Computer Science, Old Dominion University, Norfolk, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4626-4733","authenticated-orcid":false,"given":"Harold","family":"Riethman","sequence":"additional","affiliation":[{"name":"School of Medical Diagnostic and Translational Sciences, Old Dominion University, Norfolk, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8298-7093","authenticated-orcid":false,"given":"Desh","family":"Ranjan","sequence":"additional","affiliation":[{"name":"Computer Science, Old Dominion University, Norfolk, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9199-2479","authenticated-orcid":false,"given":"Pratip","family":"Rana","sequence":"additional","affiliation":[{"name":"Computer Science, Old Dominion University, Norfolk, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"e_1_3_3_2_2_2","unstructured":"Fadi Alharbi Aleksandar Vakanski Boyu Zhang Murtada\u00a0K Elbashir and Mohanad Mohammed. 2024. Comparative analysis of multi-omics integration using advanced graph neural networks for cancer classification. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2410.05325 (2024)."},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Muhtasim\u00a0Noor Alif and Wei Zhang. 2025. SynOmics: integrating multi-omics data through feature interaction networks. Briefings in Bioinformatics 26 6 (2025) bbaf595.","DOI":"10.1093\/bib\/bbaf595"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Zhaoxiang Cai Rebecca\u00a0C Poulos Jia Liu and Qing Zhong. 2022. Machine learning for multi-omics data integration in cancer. Iscience 25 2 (2022).","DOI":"10.1016\/j.isci.2022.103798"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"Antonio Colaprico Tiago\u00a0C Silva Catharina Olsen Luciano Garofano Claudia Cava Davide Garolini Thais\u00a0S Sabedot Tathiane\u00a0M Malta Stefano\u00a0M Pagnotta Isabella Castiglioni et\u00a0al. 2016. TCGAbiolinks: an R\/Bioconductor package for integrative analysis of TCGA data. Nucleic acids research 44 8 (2016) e71\u2013e71.","DOI":"10.1093\/nar\/gkv1507"},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"crossref","unstructured":"Alessio Comparini L\u00e9a Schmidt Vanessa Siffredi Damien Marie Clara James and Jonas Richiardi. 2026. Late and Early Fusion Graph Neural Network Architectures for Integrative Modeling of Multimodal Brain Connectivity Graphs. imaging 39 (2026) 38.","DOI":"10.1007\/978-3-032-19102-1_37"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Yi Ding Zhaiyue Xu Wenjing Hu Peng Deng Mian Ma and Jiandong Wu. 2025. Comprehensive multi-omics and machine learning framework for glioma subtyping and precision therapeutics. Scientific Reports 15 1 (2025) 24874.","DOI":"10.1038\/s41598-025-09742-0"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Niharika\u00a0S D\u2018Souza Hongzhi Wang Andrea Giovannini Antonio Foncubierta-Rodriguez Kristen\u00a0L Beck Orest Boyko and Tanveer\u00a0F Syeda-Mahmood. 2024. Fusing modalities by multiplexed graph neural networks for outcome prediction from medical data and beyond. Medical Image Analysis 93 (2024) 103064.","DOI":"10.1016\/j.media.2023.103064"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"crossref","unstructured":"Usman Fakhar Mohammad Elshafie and Abedalrhman Alkhateeb. 2026. GraphSAGE-based approach for age-specific multi-omics biomarker identification in bladder cancer. Network Modeling Analysis in Health Informatics and Bioinformatics 15 1 (2026) 25.","DOI":"10.1007\/s13721-025-00700-4"},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"crossref","unstructured":"Ziming Jiang Haoxuan Zhang Yibo Gao and Yingli Sun. 2025. Multi-omics strategies for biomarker discovery and application in personalized oncology. Molecular Biomedicine 6 1 (2025) 115.","DOI":"10.1186\/s43556-025-00340-0"},{"key":"e_1_3_3_2_11_2","unstructured":"Wei Ju Siyu Yi Yifan Wang Zhiping Xiao Zhengyang Mao Hourun Li Yiyang Gu Yifang Qin Nan Yin Senzhang Wang et\u00a0al. 2025. A survey of graph neural networks in real world: Imbalance noise privacy and ood challenges. IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)."},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","unstructured":"Ziynet\u00a0Nesibe Kesimoglu and Serdar Bozdag. 2023. SUPREME: multiomics data integration using graph convolutional networks. NAR Genomics and Bioinformatics 5 2 (2023) lqad063.","DOI":"10.1093\/nargab\/lqad063"},{"key":"e_1_3_3_2_13_2","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1609.02907 (2016)."},{"key":"e_1_3_3_2_14_2","first-page":"438","volume-title":"Conference of the Spanish Society of Artificial Intelligence in Biomedicine","author":"Labarga Alberto","year":"2025","unstructured":"Alberto Labarga. 2025. An Interpretable Graph Neural Network for Multi-omics Data Integration and Biomarker Discovery. In Conference of the Spanish Society of Artificial Intelligence in Biomedicine. Springer, 438\u2013448."},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"crossref","unstructured":"Xiao Li Jie Ma Ling Leng Mingfei Han Mansheng Li Fuchu He and Yunping Zhu. 2022. MoGCN: a multi-omics integration method based on graph convolutional network for cancer subtype analysis. Frontiers in Genetics 13 (2022) 806842.","DOI":"10.3389\/fgene.2022.806842"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Cheng-Pei Lin Yann-Jen Ho Yen-Peng Chiu Yun Tang You\u00a0Sheng Paik Guan-Ting Chen Wei-Chih Huang and Tzong-Yi Lee. 2026. MoAGNN: a multi-omics hierarchical graph neural network for subtype classification and prognosis prediction in lung adenocarcinoma. Briefings in Bioinformatics 27 1 (2026) bbaf735.","DOI":"10.1093\/bib\/bbaf735"},{"key":"e_1_3_3_2_17_2","unstructured":"National Cancer Institute. 2024. TCGA Prostate Adenocarcinoma (TCGA-PRAD). https:\/\/portal.gdc.cancer.gov\/projects\/TCGA-PRAD. Genomic Data Commons Data Portal accessed 2026."},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Dong Ouyang Yong Liang Le Li Ning Ai Shanghui Lu Mingkun Yu Xiaoying Liu and Shengli Xie. 2023. Integration of multi-omics data using adaptive graph learning and attention mechanism for patient classification and biomarker identification. Computers in biology and medicine 164 (2023) 107303.","DOI":"10.1016\/j.compbiomed.2023.107303"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"crossref","unstructured":"Alejandra\u00a0P P\u00e9rez-Gonz\u00e1lez Aidee\u00a0Lashmi Garc\u00eda-Kroepfly Keila\u00a0Adonai P\u00e9rez-Fuentes Roberto\u00a0Isaac Garc\u00eda-Reyes Fryda\u00a0Fernanda Solis-Roldan Jennifer\u00a0Alejandra Alba-Gonz\u00e1lez Enrique Hern\u00e1ndez-Lemus and Guillermo de Anda-J\u00e1uregui. 2024. The ROSMAP project: aging and neurodegenerative diseases through omic sciences. Frontiers in Neuroinformatics 18 (2024) 1443865.","DOI":"10.3389\/fninf.2024.1443865"},{"key":"e_1_3_3_2_20_2","unstructured":"Lisa Scarpace Tom Mikkelsen Soonmee Cha Sujaya Rao Sangeeta Tekchandani David Gutman Joel\u00a0H Saltz Bradley\u00a0J Erickson Nancy Pedano Adam\u00a0E Flanders et\u00a0al. 2016. The cancer genome atlas glioblastoma multiforme collection (TCGA-GBM). The Cancer Imaging Archive (2016)."},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Raihanul\u00a0Bari Tanvir Md\u00a0Mezbahul Islam Masrur Sobhan Dongsheng Luo and Ananda\u00a0Mohan Mondal. 2024. MOGAT: a multi-omics integration framework using graph attention networks for cancer subtype prediction. International Journal of Molecular Sciences 25 5 (2024) 2788.","DOI":"10.3390\/ijms25052788"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Rohit\u00a0K Tripathy Zachary Frohock Hong Wang Gregory\u00a0A Cary Stephen Keegan Gregory\u00a0W Carter and Yi Li. 2025. Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks. NPJ Systems Biology and Applications 11 1 (2025) 43.","DOI":"10.1038\/s41540-025-00519-9"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"crossref","unstructured":"Tongxin Wang Wei Shao Zhi Huang Haixu Tang Jie Zhang Zhengming Ding and Kun Huang. 2021. MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. Nature communications 12 1 (2021) 3445.","DOI":"10.1038\/s41467-021-23774-w"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Yating Zhong Yuzhong Peng Yanmei Lin Dingjia Chen Hao Zhang Wen Zheng Yuanyuan Chen and Changliang Wu. 2023. MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model. BMC Medical Informatics and Decision Making 23 1 (2023) 82.","DOI":"10.1186\/s12911-023-02173-9"}],"event":{"name":"BCB '26: 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","location":"Rende (CS) Italy","acronym":"BCB '26","sponsor":["SIGBio ACM Special Interest Group on Bioinformatics"]},"container-title":["Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3807503.3819442","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:11:21Z","timestamp":1785337881000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3807503.3819442"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"references-count":23,"alternative-id":["10.1145\/3807503.3819442","10.1145\/3807503"],"URL":"https:\/\/doi.org\/10.1145\/3807503.3819442","relation":{},"subject":[],"published":{"date-parts":[[2026,6,30]]},"assertion":[{"value":"2026-07-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}