{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T22:15:06Z","timestamp":1766182506172,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T00:00:00Z","timestamp":1747612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32302241","2024YFF1106705"],"award-info":[{"award-number":["32302241","2024YFF1106705"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["32302241","2024YFF1106705"],"award-info":[{"award-number":["32302241","2024YFF1106705"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Gene regulatory networks (GRNs) describe the interactions between transcription factors (TFs) and their target genes, playing a crucial role in understanding gene functions and how cells regulate gene expression under different conditions. Recent advancements in multi-omics technologies have provided new opportunities for more comprehensive GRN inference. Among these data types, gene expression and chromatin accessibility are particularly important, as they are key to distinguishing between direct and indirect regulatory relationships. However, existing methods primarily rely on gene expression data while neglecting biological information such as chromatin accessibility, leading to an increased occurrence of false positives in the inference results. To address the limitations of existing approaches, we propose MultiGNN, a supervised framework based on graph neural networks (GNNs). Unlike conventional GRN inference methods, MultiGNN leverages features extracted from both gene expression and chromatin accessibility data to predict regulatory interactions between genes. Experimental results demonstrate that MultiGNN consistently outperforms other methods across seven datasets. Additionally, ablation studies validate the effectiveness of our multi-omics feature integration strategy, offering a new direction for more accurate GRN inference.<\/jats:p>","DOI":"10.3390\/computation13050124","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T05:37:13Z","timestamp":1747633033000},"page":"124","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MultiGNN: A Graph Neural Network Framework for Inferring Gene Regulatory Networks from Single-Cell Multi-Omics Data"],"prefix":"10.3390","volume":"13","author":[{"given":"Dongbo","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Yeru","family":"Wang","sequence":"additional","affiliation":[{"name":"Risk Assessment Division 1, China National Center for Food Safety Risk Assessment, Beijing 100022, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8849","DOI":"10.1093\/nar\/gks664","article-title":"The co-regulation mechanism of transcription factors in the human gene regulatory network","volume":"40","author":"Kim","year":"2012","journal-title":"Nucleic Acids Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1038\/nrm2503","article-title":"Modelling and analysis of gene regulatory networks","volume":"9","author":"Karlebach","year":"2008","journal-title":"Nat. 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