{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T13:33:09Z","timestamp":1783776789590,"version":"3.55.0"},"reference-count":92,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T00:00:00Z","timestamp":1775347200000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["ZYGX2024Z011"],"award-info":[{"award-number":["ZYGX2024Z011"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>MicroRNAs (miRNAs) are small non-coding RNAs, typically 18\u201324 nucleotides in length, that play a pivotal role in RNA silencing and the post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Dysregulation of these miRNAs has consistently been implicated in the onset and progression of a variety of complex human diseases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this study, we propose a novel HybridGNN model that integrates a Graph Convolutional Network (GCN), a Graph Attention Network (GAT), and Matrix Decomposition with Matrix Factorization (MDMF) to predict potential miRNA\u2013disease associations (MDAs). We incorporate five types of similarity in which three are derived from miRNAs and two are derived from diseases, to comprehensively explore and optimize multi-source feature information. The complementary interactions among these modules also help to mitigate the oversmoothing problem. The model utilizes neighboring nodes in a heterogeneous network to generate node embeddings via a message-passing mechanism. To improve computational efficiency, we employ a mini-batch gradient descent approach that partitions the graph into smaller sub-graphs, thereby enhancing the model\u2019s accuracy, speed, and scalability. As a result of these advanced techniques, HybridGNN achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.9715 using a dot-product classifier, outperforming several existing methods and underscoring its potential as a robust and accurate tool for predicting MDAs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability<\/jats:title>\n                    <jats:p>Code and data are freely available at https:\/\/github.com\/mbasharatahmad\/HybridGNN-miRNA-disease\/<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag171","type":"journal-article","created":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T11:44:39Z","timestamp":1775216679000},"source":"Crossref","is-referenced-by-count":1,"title":["HybridGNN: a graph neural network approach for human miRNA\u2013disease association prediction"],"prefix":"10.1093","volume":"42","author":[{"given":"Basharat","family":"Ahmad","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China School of Life Science and Technology, , Chengdu 611731,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Hammad","family":"Musaddiq","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China School of Information and Communication Engineering, , Chengdu 611731,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebu Aboma","family":"Temesgen","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China School of Life Science and Technology, , Chengdu 611731,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grace-Mercure","family":"Bakanina Kissanga","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China School of Life Science and Technology, , Chengdu 611731,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huma","family":"Fida","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China School of Life Science and Technology, , Chengdu 611731,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6265-2862","authenticated-orcid":false,"given":"Hao","family":"Lin","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China School of Life Science and Technology, , Chengdu 611731,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3615-3415","authenticated-orcid":false,"given":"Ye-Chen","family":"Qi","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China School of Life Science and Technology, , Chengdu 611731,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,4,4]]},"reference":[{"key":"2026052023573111900_btag171-B1","doi-asserted-by":"crossref","first-page":"e1012229","DOI":"10.1371\/journal.pcbi.1012229","article-title":"MTMol-GPT: de novo multi-target molecular generation with transformer-based generative adversarial imitation learning","volume":"20","author":"Ai","year":"2024","journal-title":"PLoS Comput 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