{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T13:09:20Z","timestamp":1788786560564,"version":"build-2803163510"},"reference-count":80,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFC0906300"],"award-info":[{"award-number":["2016YFC0906300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Genetic interactions (GIs) underpin the functional connectivity of genes and pathways, and are important for dissecting genotype\u2013phenotype relationships and identifying therapeutic targets for diseases. However, the scale of the human genome restricts systematic experimental interrogation of GIs. Existing computational tools focus on predicting synthetic lethality (SL) and synthetic viability (SV), the two primary forms of GIs, yet their accuracy and biological interpretability are compromised by inadequate modeling of the molecular mechanisms behind positive and negative interactions, as well as the limitation of negative samples. To overcome these challenges, we developed Genetic Interaction Graph Convolutional Network (GiGCN), a signed network modeling framework for the joint identification of gene pairs with SL and SV. We built a high-confidence signed genetic network by integrating verified GIs, and non-interacting gene pairs, together with gene semantic similarity derived from biological processes. By leveraging disentangled subspace decomposition, this framework separately models distinct functional dimensions within gene networks, enabling robust representation of context-dependent regulatory relationships and accurate discrimination of SL and SV events. Benchmark experiments demonstrate that GiGCN outperforms state-of-the-art approaches (area under receiver operating-characteristic curve: 0.978, and area under precision\u2013recall curve: 0.944). Further analyses reveal biologically meaningful insights, including known and novel SL interactions centered on the oncogene MYC Proto-Oncogene (MYC), as well as SV interactions linked to autophagy and mitophagy pathways. This study provides a robust and interpretable network-based strategy for systematically exploring GIs. The GiGCN framework not only improves the precision of SL and SV prediction, but also offers mechanistic insights into gene functional relationships, thereby supporting the discovery of actionable therapeutic targets for cancer and other human diseases.<\/jats:p>","DOI":"10.1093\/bib\/bbag470","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T11:24:21Z","timestamp":1787570661000},"source":"Crossref","is-referenced-by-count":0,"title":["GiGCN: a network-based framework for uncovering synthetic lethal and viable genetic interactions"],"prefix":"10.1093","volume":"27","author":[{"given":"Qizheng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, Tianjin Medical University , 22 Qixiangtai Road, Tianjin, 300070 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Tianjin Medical University , 22 Qixiangtai Road, Tianjin, 300070 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengjie","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Tianjin Medical University , 22 Qixiangtai Road, Tianjin, 300070 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunyun","family":"Huo","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Tianjin Medical University , 22 Qixiangtai Road, Tianjin, 300070 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6514-0923","authenticated-orcid":false,"given":"Ju","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Tianjin Medical University , 22 Qixiangtai Road, Tianjin, 300070 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"2026090708273212700_ref1","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.cell.2011.03.020","article-title":"Genetic interactions in cancer progression and treatment","volume":"145","author":"Ashworth","year":"2011","journal-title":"Cell"},{"key":"2026090708273212700_ref2","doi-asserted-by":"publisher","first-page":"2147","DOI":"10.1093\/molbev\/msi211","article-title":"How essential are nonessential genes?","volume":"22","author":"Fang","year":"2005","journal-title":"Mol Biol Evol"},{"key":"2026090708273212700_ref3","doi-asserted-by":"crossref","first-page":"290","DOI":"10.3389\/fgene.2013.00290","article-title":"Genetic interaction networks: better understand to better predict","volume":"4","author":"Boucher","year":"2013","journal-title":"Front Genet"},{"key":"2026090708273212700_ref4","doi-asserted-by":"publisher","first-page":"20170027","DOI":"10.1515\/jib-2017-0027","article-title":"Computational approaches to identify genetic interactions for cancer therapeutics","volume":"14","author":"Benstead-Hume","year":"2017","journal-title":"J Integr Bioinform"},{"key":"2026090708273212700_ref5","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1126\/science.278.5340.1064","article-title":"Integrating genetic approaches into the discovery of anticancer drugs","volume":"278","author":"Hartwell","year":"1997","journal-title":"Science"},{"key":"2026090708273212700_ref6","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1038\/s41392-020-00358-6","article-title":"Development of synthetic lethality in cancer: molecular and cellular classification","volume":"5","author":"Li","year":"2020","journal-title":"Signal Transduct Target Ther"},{"key":"2026090708273212700_ref7","doi-asserted-by":"publisher","first-page":"eaaz4910","DOI":"10.1126\/science.aaz4910","article-title":"Genetic interaction mapping informs integrative structure determination of protein complexes","volume":"370","author":"Braberg","year":"2020","journal-title":"Science"},{"key":"2026090708273212700_ref8","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1126\/science.aam7344","article-title":"PARP inhibitors: synthetic lethality in the clinic","volume":"355","author":"Lord","year":"2017","journal-title":"Science"},{"key":"2026090708273212700_ref9","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1093\/bib\/bbw142","article-title":"A landscape of synthetic viable interactions in cancer","volume":"19","author":"Gu","year":"2018","journal-title":"Brief Bioinform"},{"key":"2026090708273212700_ref10","doi-asserted-by":"publisher","first-page":"2487","DOI":"10.1016\/j.cell.2021.03.030","article-title":"Synthetic lethality-mediated precision oncology via the tumor transcriptome","volume":"184","author":"Lee","year":"2021","journal-title":"Cell"},{"key":"2026090708273212700_ref11","doi-asserted-by":"publisher","first-page":"4209","DOI":"10.1007\/s00018-020-03519-6","article-title":"High-throughput synthetic rescue for exhaustive characterization of suppressor mutations in human genes","volume":"77","author":"Kobaisi","year":"2020","journal-title":"Cell Mol Life Sci"},{"key":"2026090708273212700_ref12","doi-asserted-by":"publisher","first-page":"e8323","DOI":"10.15252\/msb.20188323","article-title":"Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy","volume":"15","author":"Sahu","year":"2019","journal-title":"Mol Syst Biol"},{"key":"2026090708273212700_ref13","doi-asserted-by":"publisher","first-page":"12425","DOI":"10.1038\/ncomms12425","article-title":"Synthetic viability by BRCA2 and PARP1\/ARTD1 deficiencies","volume":"7","author":"Ding","year":"2016","journal-title":"Nat Commun"},{"key":"2026090708273212700_ref14","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1016\/j.cell.2017.07.005","article-title":"Project DRIVE: a compendium of cancer dependencies and synthetic lethal relationships uncovered by large-scale, deep RNAi screening","volume":"170","author":"McDonald","year":"2017","journal-title":"Cell"},{"key":"2026090708273212700_ref15","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1126\/science.1255648","article-title":"Translational genomics. Clues from the resilient","volume":"344","author":"Friend","year":"2014","journal-title":"Science"},{"key":"2026090708273212700_ref16","doi-asserted-by":"publisher","first-page":"baac030","DOI":"10.1093\/database\/baac030","article-title":"SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery","volume":"2022","author":"Wang","year":"2022","journal-title":"Database (Oxford)"},{"key":"2026090708273212700_ref17","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaf1420","article-title":"A global genetic interaction network maps a wiring diagram of cellular function","volume":"353","author":"Costanzo","year":"2016","journal-title":"Science"},{"key":"2026090708273212700_ref18","doi-asserted-by":"publisher","first-page":"e2404332122","DOI":"10.1073\/pnas.2404332122","article-title":"Organismal complexity strongly correlates with the number of protein families and domains","volume":"122","author":"Alvarez-Ponce","year":"2025","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2026090708273212700_ref19","doi-asserted-by":"publisher","first-page":"e1012336","DOI":"10.1371\/journal.pcbi.1012336","article-title":"MAGICAL: a multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network","volume":"20","author":"Dey","year":"2024","journal-title":"PLoS Comput Biol"},{"key":"2026090708273212700_ref20","doi-asserted-by":"publisher","first-page":"1687","DOI":"10.1002\/pmic.201100607","article-title":"Combining many interaction networks to predict gene function and analyze gene lists","volume":"12","author":"Mostafavi","year":"2012","journal-title":"Proteomics"},{"key":"2026090708273212700_ref21","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1126\/science.1123287","article-title":"Genome-wide prediction of C. elegans genetic interactions","volume":"311","author":"Zhong","year":"2006","journal-title":"Science"},{"key":"2026090708273212700_ref22","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1186\/1471-2105-9-426","article-title":"Mining protein networks for synthetic genetic interactions","volume":"9","author":"Paladugu","year":"2008","journal-title":"BMC Bioinform"},{"key":"2026090708273212700_ref23","doi-asserted-by":"publisher","first-page":"e1006888","DOI":"10.1371\/journal.pcbi.1006888","article-title":"Predicting synthetic lethal interactions using conserved patterns in protein interaction networks","volume":"15","author":"Benstead-Hume","year":"2019","journal-title":"PLoS Comput Biol"},{"key":"2026090708273212700_ref24","doi-asserted-by":"publisher","first-page":"71","DOI":"10.4137\/CIN.S14026","article-title":"In silico prediction of synthetic lethality by meta-analysis of genetic interactions, functions, and pathways in yeast and human cancer","volume":"13","author":"Wu","year":"2014","journal-title":"Cancer Inform"},{"key":"2026090708273212700_ref25","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1002\/jcb.27395","article-title":"Identification of synthetic lethality based on a functional network by using machine learning algorithms","volume":"120","author":"Li","year":"2019","journal-title":"J Cell Biochem"},{"key":"2026090708273212700_ref26","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1186\/s12943-021-01405-8","article-title":"Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality","volume":"20","author":"Benfatto","year":"2021","journal-title":"Mol Cancer"},{"key":"2026090708273212700_ref27","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1186\/1755-8794-6-30","article-title":"Identification of potential synthetic lethal genes to p53 using a computational biology approach","volume":"6","author":"Wang","year":"2013","journal-title":"BMC Med Genet"},{"key":"2026090708273212700_ref28","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s11390-021-0866-2","article-title":"Synthetic lethal interactions prediction based on multiple similarity measures fusion","volume":"36","author":"Wu","year":"2021","journal-title":"J Comput Sci Technol"},{"key":"2026090708273212700_ref29","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1186\/s12859-019-3197-3","article-title":"Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization","volume":"20","author":"Huang","year":"2019","journal-title":"BMC Bioinform"},{"key":"2026090708273212700_ref30","doi-asserted-by":"publisher","first-page":"2209","DOI":"10.1093\/bioinformatics\/btz893","article-title":"Predicting synthetic lethal interactions using heterogeneous data sources","volume":"36","author":"Liany","year":"2020","journal-title":"Bioinformatics"},{"key":"2026090708273212700_ref31","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1109\/TCBB.2019.2909908","article-title":"SL(2)MF: predicting synthetic lethality in human cancers via logistic matrix factorization","volume":"17","author":"Liu","year":"2020","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2026090708273212700_ref32","doi-asserted-by":"publisher","first-page":"ii106","DOI":"10.1093\/bioinformatics\/btac476","article-title":"PiLSL: pairwise interaction learning-based graph neural network for synthetic lethality prediction in human cancers","volume":"38","author":"Liu","year":"2022","journal-title":"Bioinformatics"},{"key":"2026090708273212700_ref33","doi-asserted-by":"publisher","first-page":"108669","DOI":"10.1016\/j.compbiomed.2024.108669","article-title":"DSSGNN-PPI: a protein-protein interactions prediction model based on double structure and sequence graph neural networks","volume":"177","author":"Zhang","year":"2024","journal-title":"Comput Biol Med"},{"key":"2026090708273212700_ref34","doi-asserted-by":"publisher","first-page":"8360","DOI":"10.1038\/s41598-022-12201-9","article-title":"Prediction of protein-protein interaction using graph neural networks","volume":"12","author":"Jha","year":"2022","journal-title":"Sci Rep"},{"key":"2026090708273212700_ref35","doi-asserted-by":"publisher","first-page":"3004","DOI":"10.3390\/molecules27093004","article-title":"Prediction of drug-drug interaction using an attention-based graph neural network on drug molecular graphs","volume":"27","author":"Feng","year":"2022","journal-title":"Molecules"},{"key":"2026090708273212700_ref36","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1089\/cmb.2022.0113","article-title":"SGFNNs: signed graph filtering-based neural networks for predicting drug-drug interactions","volume":"29","author":"Chen","year":"2022","journal-title":"J Comput Biol"},{"key":"2026090708273212700_ref37","doi-asserted-by":"publisher","first-page":"bbab051","DOI":"10.1093\/bib\/bbab051","article-title":"NPI-GNN: predicting ncRNA-protein interactions with deep graph neural networks","volume":"22","author":"Shen","year":"2021","journal-title":"Brief Bioinform"},{"key":"2026090708273212700_ref38","doi-asserted-by":"publisher","first-page":"22496","DOI":"10.1021\/acsomega.3c00085","article-title":"SS-GNN: a simple-structured graph neural network for affinity prediction","volume":"8","author":"Zhang","year":"2023","journal-title":"ACS Omega"},{"key":"2026090708273212700_ref39","doi-asserted-by":"publisher","first-page":"4131","DOI":"10.1021\/acs.jcim.9b00628","article-title":"Graph convolutional neural networks for predicting drug-target interactions","volume":"59","author":"Torng","year":"2019","journal-title":"J Chem Inf Model"},{"key":"2026090708273212700_ref40","doi-asserted-by":"publisher","first-page":"4458","DOI":"10.1093\/bioinformatics\/btaa211","article-title":"Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers","volume":"36","author":"Cai","year":"2020","journal-title":"Bioinformatics"},{"key":"2026090708273212700_ref41","doi-asserted-by":"publisher","first-page":"2432","DOI":"10.1093\/bioinformatics\/btab110","article-title":"Graph contextualized attention network for predicting synthetic lethality in human cancers","volume":"37","author":"Long","year":"2021","journal-title":"Bioinformatics"},{"key":"2026090708273212700_ref42","doi-asserted-by":"publisher","first-page":"2681","DOI":"10.1109\/TCBB.2022.3221736","article-title":"MAGCN: a multiple attention graph convolution networks for predicting synthetic lethality","volume":"20","author":"Lu","year":"2023","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2026090708273212700_ref43","doi-asserted-by":"crossref","first-page":"1103092","DOI":"10.3389\/fgene.2022.1103092","article-title":"Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction","volume":"13","author":"Fan","year":"2022","journal-title":"Front Genet"},{"key":"2026090708273212700_ref44","doi-asserted-by":"publisher","first-page":"btad015","DOI":"10.1093\/bioinformatics\/btad015","article-title":"SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network","volume":"39","author":"Zhu","year":"2023","journal-title":"Bioinformatics"},{"key":"2026090708273212700_ref45","doi-asserted-by":"publisher","first-page":"107478","DOI":"10.1016\/j.isci.2023.107478","article-title":"KS-CMI: a circRNA-miRNA interaction prediction method based on the signed graph neural network and denoising autoencoder","volume":"26","author":"Wang","year":"2023","journal-title":"iScience"},{"key":"2026090708273212700_ref46","doi-asserted-by":"publisher","first-page":"bbaf062","DOI":"10.1093\/bib\/bbaf062","article-title":"CSGDN: contrastive signed graph diffusion network for predicting crop gene-phenotype associations","volume":"26","author":"Pan","year":"2024","journal-title":"Brief Bioinform"},{"key":"2026090708273212700_ref47","doi-asserted-by":"publisher","first-page":"bbab464","DOI":"10.1093\/bib\/bbab464","article-title":"SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations","volume":"23","author":"Zhang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2026090708273212700_ref48","doi-asserted-by":"publisher","first-page":"216991","DOI":"10.1016\/j.canlet.2024.216991","article-title":"Harnessing genetic interactions for prediction of immune checkpoint inhibitors response signature in cancer cells","volume":"594","author":"Liu","year":"2024","journal-title":"Cancer Lett"},{"key":"2026090708273212700_ref49","doi-asserted-by":"publisher","first-page":"4041","DOI":"10.1109\/JBHI.2021.3079302","article-title":"Prediction of synthetic lethal interactions in human cancers using multi-view graph auto-encoder","volume":"25","author":"Hao","year":"2021","journal-title":"IEEE J Biomed Health Inform"},{"key":"2026090708273212700_ref50","doi-asserted-by":"publisher","first-page":"i418","DOI":"10.1093\/bioinformatics\/btab271","article-title":"KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers","volume":"37","author":"Wang","year":"2021","journal-title":"Bioinformatics"},{"key":"2026090708273212700_ref51","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1093\/bioinformatics\/btq064","article-title":"GOSemSim: an R package for measuring semantic similarity among GO terms and gene products","volume":"26","author":"Yu","year":"2010","journal-title":"Bioinformatics"},{"key":"2026090708273212700_ref52","doi-asserted-by":"publisher","first-page":"35638","DOI":"10.1038\/s41598-025-19502-9","article-title":"Research on signed directed network link prediction based on dual attention mechanism","volume":"15","author":"Chen","year":"2025","journal-title":"Sci Rep"},{"key":"2026090708273212700_ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38","volume-title":"The Semantic Web","author":"Schlichtkrull","year":"2018"},{"key":"2026090708273212700_ref54","first-page":"1","article-title":"Norm augmented graph AutoEncoders for link prediction","volume-title":"Proceedings of the IEEE International Conference on Acoustics, Speech, Signal Processing (ICASSP)","author":"Liu"},{"key":"2026090708273212700_ref55","doi-asserted-by":"publisher","first-page":"1336","DOI":"10.1109\/TKDE.2012.51","article-title":"Nonnegative matrix factorization: a comprehensive review","volume":"25","author":"Wang","year":"2013","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2026090708273212700_ref56","doi-asserted-by":"publisher","first-page":"103633","DOI":"10.1016\/j.mex.2025.103633","article-title":"High performance GPU implementation of KNN algorithm: a review","volume":"15","author":"Bidye","year":"2025","journal-title":"MethodsX"},{"key":"2026090708273212700_ref57","first-page":"855","article-title":"node2vec: scalable feature learning for networks","volume":"2016","author":"Grover","year":"2016","journal-title":"KDD"},{"key":"2026090708273212700_ref58","doi-asserted-by":"publisher","first-page":"930","DOI":"10.1101\/gr.275870.121","article-title":"Chromatin interaction-aware gene regulatory modeling with graph attention networks","volume":"32","author":"Karbalayghareh","year":"2022","journal-title":"Genome Res"},{"key":"2026090708273212700_ref59","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1007\/978-3-031-24628-9_16","volume-title":"Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook","author":"Bank","year":"2023"},{"key":"2026090708273212700_ref60","doi-asserted-by":"crossref","first-page":"2827","DOI":"10.1145\/3459637.3482215","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Ahn","year":"2021"},{"key":"2026090708273212700_ref61","first-page":"1025","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Hamilton","year":"2017"},{"key":"2026090708273212700_ref62","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/978-3-319-93417-4_38","volume-title":"The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3\u20137, 2018, Proceedings","author":"Schlichtkrull","year":"2018"},{"key":"2026090708273212700_ref63","first-page":"2071","volume-title":"Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48","author":"Trouillon","year":"2016"},{"key":"2026090708273212700_ref64","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1007\/978-3-030-30493-5_53","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Workshop and Special Sessions: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17\u201319, 2019, Proceedings","author":"Huang","year":"2019"},{"key":"2026090708273212700_ref65","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.neucom.2018.08.072","article-title":"Learning node and edge embeddings for signed networks","volume":"319","author":"Song","year":"2018","journal-title":"Neurocomputing"},{"key":"2026090708273212700_ref66","doi-asserted-by":"publisher","first-page":"111026","DOI":"10.1016\/j.patcog.2024.111026","article-title":"SiGNN: a spike-induced graph neural network for dynamic graph representation learning","volume":"158","author":"Chen","year":"2025","journal-title":"Pattern Recogn"},{"key":"2026090708273212700_ref67","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1126\/science.1212728","article-title":"A SUMOylation-dependent transcriptional subprogram is required for Myc-driven tumorigenesis","volume":"335","author":"Kessler","year":"2012","journal-title":"Science"},{"key":"2026090708273212700_ref68","first-page":"330","article-title":"SUMO-activating SAE1 transcription is positively regulated by Myc","volume":"2","author":"Amente","year":"2012","journal-title":"Am J Cancer Res"},{"key":"2026090708273212700_ref69","doi-asserted-by":"publisher","first-page":"4246","DOI":"10.1128\/JVI.79.7.4246-4256.2005","article-title":"Silencing of integrated human papillomavirus type 18 oncogene transcription in cells expressing SerpinB2","volume":"79","author":"Darnell","year":"2005","journal-title":"J Virol"},{"key":"2026090708273212700_ref70","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1124\/pharmrev.122.000795","article-title":"The Myc family and the metastasis suppressor NDRG1: targeting key molecular interactions with innovative therapeutics","volume":"75","author":"Deng","year":"2023","journal-title":"Pharmacol Rev"},{"key":"2026090708273212700_ref71","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.2337\/diabetes.51.6.1793","article-title":"Overexpression of c-Myc in beta-cells of transgenic mice causes proliferation and apoptosis, downregulation of insulin gene expression, and diabetes","volume":"51","author":"Laybutt","year":"2002","journal-title":"Diabetes"},{"key":"2026090708273212700_ref72","doi-asserted-by":"publisher","first-page":"e2413120","DOI":"10.1002\/advs.202413120","article-title":"Squamocin suppresses tumor growth through triggering an endoplasmic reticulum stress-associated degradation of EZH2\/MYC axis","volume":"12","author":"Zhu","year":"2025","journal-title":"Adv Sci (Weinh)"},{"key":"2026090708273212700_ref73","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.taap.2017.09.005","article-title":"Downregulation of UBE2E2 in rat liver cells after hepatocarcinogen treatment facilitates cell proliferation and slowing down of DNA damage response in GST-P-expressing preneoplastic lesions","volume":"334","author":"Mizukami","year":"2017","journal-title":"Toxicol Appl Pharmacol"},{"key":"2026090708273212700_ref74","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1002\/jcp.31145","article-title":"NDP52 SUMOylation contributes to low-dose X-rays-induced cardiac hypertrophy through PINK1\/parkin-mediated mitophagy via MUL1\/SUMO2 signalling","volume":"239","author":"Gao","year":"2024","journal-title":"J Cell Physiol"},{"key":"2026090708273212700_ref75","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1038\/s41419-019-1752-5","article-title":"mTOR hyperactivation in down syndrome underlies deficits in autophagy induction, autophagosome formation, and mitophagy","volume":"10","author":"Bordi","year":"2019","journal-title":"Cell Death Dis"},{"key":"2026090708273212700_ref76","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1038\/s41392-022-01253-y","article-title":"Targeting CRL4 suppresses chemoresistant ovarian cancer growth by inducing mitophagy","volume":"7","author":"Meng","year":"2022","journal-title":"Signal Transduct Target Ther"},{"key":"2026090708273212700_ref77","doi-asserted-by":"publisher","first-page":"109597","DOI":"10.1016\/j.celrep.2021.109597","article-title":"Discovery of synthetic lethal and tumor suppressor paralog pairs in the human genome","volume":"36","author":"Parrish","year":"2021","journal-title":"Cell Rep"},{"key":"2026090708273212700_ref78","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.ygyno.2025.04.580","article-title":"Maintenance olaparib monotherapy in patients with platinum-sensitive relapsed ovarian cancer without a germline BRCA1 and\/or BRCA2 mutation: final overall survival results from the OPINION trial","volume":"197","author":"Poveda","year":"2025","journal-title":"Gynecol Oncol"},{"key":"2026090708273212700_ref79","doi-asserted-by":"publisher","first-page":"e0258596","DOI":"10.1371\/journal.pone.0258596","article-title":"The structure balance of gene-gene networks beyond pairwise interactions","volume":"17","author":"Allahyari","year":"2022","journal-title":"PLoS One"},{"key":"2026090708273212700_ref80","doi-asserted-by":"crossref","first-page":"1877","DOI":"10.1145\/3341105.3374048","volume-title":"Proceedings of the 35th Annual ACM Symposium on Applied Computing","author":"Rizi","year":"2020"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/5\/bbag470\/70981142\/bbag470.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/5\/bbag470\/70981142\/bbag470.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T12:27:40Z","timestamp":1788784060000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbag470\/8787135"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,1]]},"references-count":80,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,9,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbag470","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,9]]},"published":{"date-parts":[[2026,9,1]]},"article-number":"bbag470"}}