{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:31:00Z","timestamp":1762957860321,"version":"3.45.0"},"reference-count":104,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"vor","delay-in-days":11,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Molecular subtyping is fundamental in cancer research and clinical management of cancer, guiding treatment planning, monitoring therapeutic response, and informing prognosis. Early methods were designed specifically for gene expression data due to the lack of other molecular data types. Thanks to breakthroughs in high-throughput technologies, recent subtyping tools have shifted their focus to integrating multi-omics profiles to uncover novel subtypes that better reflect genetic variation, molecular pathogenesis, tumor heterogeneity, and host response biological mechanisms. However, these integrative approaches have not been able to fully exploit the complementary potentials of diverse molecular data types. They often rely on specific omics types with large common sample size and fail to incorporate important biological knowledge in their models. Here, we introduce Disease subtyping using Spectral clustering and Community detection from Consensus networks (DSCC), a method designed to identify meaningful disease subtypes from a wide range of molecular data, including gene expression, miRNA expression, DNA methylation, copy number variation, somatic mutations, protein abundance, and metabolite levels. We demonstrate the superiority of DSCC over state-of-the-art cancer subtyping methods using 43 cancer datasets with more than 11,000 patients. Furthermore, the incorporation of DSCC-derived subtype information as a covariate in prognostic models improves survival prediction accuracy and robustness. The DSCC source code, data, and scripts for reproducing all results in this study are available at https:\/\/github.com\/tinnlab\/DSCC.<\/jats:p>","DOI":"10.1093\/bib\/bbaf600","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:28:13Z","timestamp":1762957693000},"source":"Crossref","is-referenced-by-count":0,"title":["DSCC: disease subtyping using spectral clustering and community detection from consensus networks"],"prefix":"10.1093","volume":"26","author":[{"given":"Dao","family":"Tran","sequence":"first","affiliation":[{"name":"Department of Computer Science and Software Engineering , Auburn University, Auburn, 36849 Alabama,","place":["United States"]}]},{"given":"Van-Dung","family":"Pham","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering , Auburn University, Auburn, 36849 Alabama,","place":["United States"]}]},{"given":"Ha","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering , Auburn University, Auburn, 36849 Alabama,","place":["United States"]}]},{"given":"Phi","family":"Bya","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering , Auburn University, Auburn, 36849 Alabama,","place":["United States"]}]},{"given":"Aiham","family":"Qdaisat","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine , The University of Texas MD Anderson Cancer Center, Houston, 77030 Texas,","place":["United States"]}]},{"given":"Liem Minh","family":"Phan","sequence":"additional","affiliation":[{"name":"David Grant USAF Medical Center \u2013 Clinical Investigation Facility , 60 th Medical Group, Defense Health Agency, Travis Air Force Base, 94535 California,","place":["United States"]}]},{"given":"Sai-Ching Jim","family":"Yeung","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine , The University of Texas MD Anderson Cancer Center, Houston, 77030 Texas,","place":["United States"]}]},{"given":"Tin","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering , Auburn University, Auburn, 36849 Alabama,","place":["United States"]}]}],"member":"286","published-online":{"date-parts":[[2025,11,12]]},"reference":[{"key":"2025111209280626400_ref1","doi-asserted-by":"publisher","first-page":"423","DOI":"10.3389\/fonc.2020.00423","article-title":"Computational oncology in the multi-omics era: State of the art","volume":"10","author":"de Anda-J\u00e1uregui","year":"2020","journal-title":"Front Oncol"},{"key":"2025111209280626400_ref2","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1016\/j.csbj.2021.01.009","article-title":"Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis","volume":"19","author":"Menyh\u00e1rt","year":"2021","journal-title":"Comput Struct Biotechnol J"},{"key":"2025111209280626400_ref3","doi-asserted-by":"publisher","DOI":"10.3390\/medsci11030044","article-title":"Multi-omics data analysis identifies prognostic biomarkers across cancers","volume":"11","author":"Karaman","year":"2023","journal-title":"Medical Sciences"},{"key":"2025111209280626400_ref4","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1038\/nbt.4017","article-title":"Precision oncology in the age of integrative genomics","volume":"36","author":"Kumar-Sinha","year":"2018","journal-title":"Nat Biotechnol"},{"key":"2025111209280626400_ref5","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1016\/j.molmed.2017.08.003","article-title":"Precision oncology: The road ahead","volume":"23","author":"Senft","year":"2017","journal-title":"Trends Mol Med"},{"key":"2025111209280626400_ref6","doi-asserted-by":"publisher","first-page":"1458","DOI":"10.1038\/s41587-019-0332-7","article-title":"Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia","volume":"37","author":"Granja","year":"2019","journal-title":"Nat Biotechnol"},{"key":"2025111209280626400_ref7","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1038\/nature10983","article-title":"The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups","volume":"486","author":"Curtis","year":"2012","journal-title":"Nature"},{"key":"2025111209280626400_ref8","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1158\/1078-0432.CCR-14-0432","article-title":"Comprehensive genomic analysis identifies novel subtypes and targets of triple-negative breast cancer","volume":"21","author":"Burstein","year":"2015","journal-title":"Clin Cancer Res"},{"key":"2025111209280626400_ref9","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1158\/1078-0432.CCR-17-0853","article-title":"Deep learning\u2013based multi-omics integration robustly predicts survival in liver cancer","volume":"24","author":"Chaudhary","year":"2018","journal-title":"Clin Cancer Res"},{"key":"2025111209280626400_ref10","doi-asserted-by":"publisher","first-page":"2231","DOI":"10.1093\/bioinformatics\/btab109","article-title":"Subtype-GAN: A deep learning approach for integrative cancer subtyping of multi-omics data","volume":"37","author":"Yang","year":"2021","journal-title":"Bioinformatics"},{"key":"2025111209280626400_ref11","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-55068-2","article-title":"Multi-omics analyses reveal biological and clinical insights in recurrent stage I non-small cell lung cancer","volume":"16","author":"Wang","year":"2025","journal-title":"Nat Commun"},{"key":"2025111209280626400_ref12","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1186\/s12885-021-07888-4","article-title":"Multi-omics analysis of genomics, epigenomics and transcriptomics for molecular subtypes and core genes for lung adenocarcinoma","volume":"21","author":"Zhao","year":"2021","journal-title":"BMC Cancer"},{"key":"2025111209280626400_ref13","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1038\/s43018-022-00510-x","article-title":"Integrative multi-omics networks identify PKC$\\delta $ and DNA-PK as master kinases of glioblastoma subtypes and guide targeted cancer therapy","volume":"4","author":"Migliozzi","year":"2023","journal-title":"Nature Cancer"},{"key":"2025111209280626400_ref14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1098\/rsfs.2020.0072","article-title":"Glioblastoma multiforme: A multi-omics analysis of driver genes and tumour heterogeneity","volume":"11","author":"Herrera-Oropeza","year":"2021","journal-title":"Interface Focus"},{"key":"2025111209280626400_ref15","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-22465-w","article-title":"Aur\u00e9lien de Reyni\u00e8s, roman Nawroth, and Lars Dyrskj\u00f8t. An integrated multi-omics analysis identifies prognostic molecular subtypes of non-muscle-invasive bladder cancer. Nature","volume":"12","author":"Lindskrog","year":"2021","journal-title":"Communications"},{"key":"2025111209280626400_ref16","article-title":"Integrated multi-omics approach to distinct molecular characterization and classification of early-onset colorectal cancer","volume":"4","author":"Mulong","year":"2023","journal-title":"Cell Reports Medicine"},{"key":"2025111209280626400_ref17","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2023.1196892","article-title":"Innovative breakthroughs facilitated by single-cell multi-omics: Manipulating natural killer cell functionality correlates with a novel subcategory of melanoma cells","volume":"14","author":"Zhao","year":"2023","journal-title":"Front Immunol"},{"key":"2025111209280626400_ref18","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.celrep.2016.12.019","article-title":"Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade","volume":"18","author":"Charoentong","year":"2017","journal-title":"Cell Rep"},{"key":"2025111209280626400_ref19","doi-asserted-by":"publisher","first-page":"2610","DOI":"10.1093\/bioinformatics\/btt425","article-title":"Bayesian consensus clustering","volume":"29","author":"Lock","year":"2013","journal-title":"Bioinformatics"},{"key":"2025111209280626400_ref20","doi-asserted-by":"publisher","first-page":"3290","DOI":"10.1093\/bioinformatics\/bts595","article-title":"Bayesian correlated clustering to integrate multiple datasets","volume":"28","author":"Kirk","year":"2012","journal-title":"Bioinformatics"},{"key":"2025111209280626400_ref21","first-page":"5539","article-title":"MOVICS: An R package for multi-omics integration and visualization in cancer subtyping","volume":"36","author":"Xiaofan","year":"2020","journal-title":"Bioinformatics"},{"key":"2025111209280626400_ref22","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1186\/s12859-021-04279-1","article-title":"Consensus clustering applied to multi-omics disease subtyping","volume":"22","author":"Bri\u00e8re","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"2025111209280626400_ref23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbab398","article-title":"Subtype-WESLR: Identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data","volume":"23","author":"Song","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025111209280626400_ref24","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0176278","article-title":"Integrative clustering of multi-level \u2018omic data based on non-negative matrix factorization algorithm","volume":"12","author":"Chalise","year":"2017","journal-title":"PloS One"},{"key":"2025111209280626400_ref25","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1186\/s12864-015-2223-8","article-title":"Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: Application to cancer molecular classification","volume":"16","author":"Dingming","year":"2015","journal-title":"BMC Genomics"},{"key":"2025111209280626400_ref26","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1093\/biostatistics\/kxx017","article-title":"A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data","volume":"19","author":"Mo","year":"2018","journal-title":"Biostatistics"},{"key":"2025111209280626400_ref27","doi-asserted-by":"crossref","first-page":"4245","DOI":"10.1073\/pnas.1208949110","article-title":"Pattern discovery and cancer gene identification in integrated cancer genomic data","volume":"110","author":"Mo","year":"2013","journal-title":"Proc Natl Acad Sci"},{"key":"2025111209280626400_ref28","doi-asserted-by":"publisher","first-page":"2906","DOI":"10.1093\/bioinformatics\/btp543","article-title":"Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis","volume":"25","author":"Shen","year":"2009","journal-title":"Bioinformatics"},{"key":"2025111209280626400_ref29","doi-asserted-by":"publisher","first-page":"e35236","DOI":"10.1371\/journal.pone.0035236","article-title":"Integrative subtype discovery in glioblastoma using iCluster","volume":"7","author":"Shen","year":"2012","journal-title":"PloS One"},{"key":"2025111209280626400_ref30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bioinformatics\/btad353","article-title":"MRGCN: Cancer subtyping with multi-reconstruction graph convolutional network using full and partial multi-omics dataset","volume":"39","author":"Yang","year":"2023","journal-title":"Bioinformatics"},{"key":"2025111209280626400_ref31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbab600","article-title":"Deep latent space fusion for adaptive representation of heterogeneous multi-omics data","volume":"23","author":"Zhang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025111209280626400_ref32","doi-asserted-by":"publisher","first-page":"3337","DOI":"10.1093\/bioinformatics\/btac345","article-title":"Deep structure integrative representation of multi-omics data for cancer subtyping","volume":"38","author":"Yang","year":"2022","journal-title":"Bioinformatics"},{"key":"2025111209280626400_ref33","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1038\/nmeth.2810","article-title":"Similarity network fusion for aggregating data types on a genomic scale","volume":"11","author":"Wang","year":"2014","journal-title":"Nat Methods"},{"key":"2025111209280626400_ref34","doi-asserted-by":"publisher","first-page":"3348","DOI":"10.1093\/bioinformatics\/btz058","article-title":"NEMO: Cancer subtyping by integration of partial multi-omic data","volume":"35","author":"Rappoport","year":"2019","journal-title":"Bioinformatics"},{"key":"2025111209280626400_ref35","article-title":"PINSPlus: Clustering algorithm for data integration and disease subtyping","author":"Nguyen","year":"2020","journal-title":"R package version"},{"key":"2025111209280626400_ref36","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.1101\/gr.215129.116","article-title":"A novel approach for data integration and disease subtyping","volume":"27","author":"Nguyen","year":"2017","journal-title":"Genome Res"},{"key":"2025111209280626400_ref37","article-title":"Integrated cancer subtyping using heterogeneous genome-scale molecular datasets","volume-title":"Pacific Symposium on Biocomputing","author":"Arslanturk","year":"2020"},{"key":"2025111209280626400_ref38","doi-asserted-by":"publisher","first-page":"4453","DOI":"10.1038\/s41467-018-06921-8","article-title":"Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival","volume":"9","author":"Ramazzotti","year":"2018","journal-title":"Nat Commun"},{"key":"2025111209280626400_ref39","doi-asserted-by":"crossref","DOI":"10.1109\/BIBM.2017.8217682","article-title":"Integrate multi-omic data using affinity network fusion (ANF) for cancer patient clustering","volume-title":"2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Ma","year":"2017"},{"key":"2025111209280626400_ref40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbac488","article-title":"Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning","volume":"24","author":"Wei","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025111209280626400_ref41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbac132","article-title":"MDICC: Novel method for multi-omics data integration and cancer subtype identification","volume":"23","author":"Yang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025111209280626400_ref42","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2017.713","article-title":"Sphereface: Deep hypersphere embedding for face recognition","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Liu"},{"key":"2025111209280626400_ref43","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2018.00552","article-title":"Cosface: Large margin cosine loss for deep face recognition","volume-title":"Proceedings of the IEEE Conference on Computer vision and Pattern Recognition","author":"Wang"},{"key":"2025111209280626400_ref44","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2019.00482","article-title":"Arcface: Additive angular margin loss for deep face recognition","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Deng"},{"key":"2025111209280626400_ref45","first-page":"849","article-title":"On spectral clustering: Analysis and an algorithm","volume":"2","author":"Andrew","year":"2002","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2025111209280626400_ref46","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast unfolding of communities in large networks","volume":"2008","author":"Blondel","year":"2008","journal-title":"Journal of Statistical Mechanics: Theory and Experiment"},{"key":"2025111209280626400_ref47","doi-asserted-by":"publisher","first-page":"D353","DOI":"10.1093\/nar\/gkw1092","article-title":"KEGG: New perspectives on genomes, pathways, diseases and drugs","volume":"45","author":"Kanehisa","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2025111209280626400_ref48","doi-asserted-by":"publisher","first-page":"D619","DOI":"10.1093\/nar\/gkn863","article-title":"Reactome knowledgebase of human biological pathways and processes","volume":"37","author":"Matthews","year":"2009","journal-title":"Nucleic Acids Res"},{"key":"2025111209280626400_ref49","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/A:1023949509487","article-title":"Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data","volume":"52","author":"Monti","year":"2003","journal-title":"Machine Learning"},{"journal-title":"GDC Data Portal","article-title":"Genomic data commons","year":"2021","key":"2025111209280626400_ref50"},{"key":"2025111209280626400_ref51","doi-asserted-by":"publisher","first-page":"2213","DOI":"10.1101\/gr.124321.111","article-title":"Differential expression in rna-seq: A matter of depth","volume":"21","author":"Tarazona","year":"2011","journal-title":"Genome Res"},{"key":"2025111209280626400_ref52","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1186\/1471-2105-12-480","article-title":"GC-content normalization for RNA-Seq data","volume":"12","author":"Risso","year":"2011","journal-title":"BMC Bioinformatics"},{"key":"2025111209280626400_ref53","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1093\/bib\/bbx008","article-title":"Selecting between-sample RNA-seq normalization methods from the perspective of their assumptions","volume":"19","author":"Evans","year":"2018","journal-title":"Brief Bioinform"},{"key":"2025111209280626400_ref54","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1261\/rna.074922.120","article-title":"Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols","volume":"26","author":"Zhao","year":"2020","journal-title":"RNA"},{"key":"2025111209280626400_ref55","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1186\/1471-2105-11-94","article-title":"Evaluation of statistical methods for normalization and differential expression in mRNA-seq experiments","volume":"11","author":"Bullard","year":"2010","journal-title":"BMC bioinformatics"},{"key":"2025111209280626400_ref56","doi-asserted-by":"publisher","first-page":"e0321631","DOI":"10.1371\/journal.pone.0321631","article-title":"Reliable rna-seq analysis from ffpe specimens as a means to accelerate cancer-related health disparities research","volume":"20","author":"Frederick","year":"2025","journal-title":"PloS One"},{"key":"2025111209280626400_ref57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12967-021-02936-w","article-title":"A comparative study of quantification measures for the analysis of RNA-seq data from the NCI patient-derived models repository","volume":"19","author":"Zhao","year":"2021","journal-title":"J Transl Med"},{"key":"2025111209280626400_ref58","doi-asserted-by":"publisher","first-page":"1905","DOI":"10.1101\/gr.122135.111","article-title":"Digital gene expression for non-model organisms","volume":"21","author":"Hong","year":"2011","journal-title":"Genome Res"},{"key":"2025111209280626400_ref59","doi-asserted-by":"publisher","first-page":"D147","DOI":"10.1093\/nar\/gkae1072","article-title":"miRTarBase 2025: Updates to the collection of experimentally validated microRNA\u2013target interactions","volume":"53","author":"Cui","year":"2025","journal-title":"Nucleic Acids Res"},{"key":"2025111209280626400_ref60","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1631\/jzus.A0720058","article-title":"Local and global approaches of affinity propagation clustering for large scale data","volume":"9","author":"Xia","year":"2008","journal-title":"Journal of Zhejiang University-Science A"},{"key":"2025111209280626400_ref61","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2014.188","article-title":"Constructing robust affinity graphs for spectral clustering","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Zhu"},{"key":"2025111209280626400_ref62","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2012.6247748","article-title":"Affinity aggregation for spectral clustering","volume-title":"2012 IEEE Conference on computer vision and pattern recognition","author":"Huang","year":"2012"},{"key":"2025111209280626400_ref63","doi-asserted-by":"publisher","first-page":"8577","DOI":"10.1073\/pnas.0601602103","volume":"103","author":"Mark","year":"2006","journal-title":"Modularity and community structure in networks Proceedings of the National Academy of Sciences"},{"key":"2025111209280626400_ref64","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"Journal of Computational and Applied Mathematics"},{"key":"2025111209280626400_ref65","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1038\/s42255-023-00817-8","article-title":"A multimodal atlas of tumour metabolism reveals the architecture of gene\u2013metabolite covariation","volume":"5","author":"Benedetti","year":"2023","journal-title":"Nat Metab"},{"key":"2025111209280626400_ref66","doi-asserted-by":"publisher","first-page":"4983","DOI":"10.1158\/1078-0432.CCR-13-0209","article-title":"Integration of metabolomics and transcriptomics revealed a fatty acid network exerting growth inhibitory effects in human pancreatic cancer","volume":"19","author":"Zhang","year":"2013","journal-title":"Clin Cancer Res"},{"key":"2025111209280626400_ref67","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1172\/JCI71180","article-title":"Myc-driven accumulation of 2-hydroxyglutarate is associated with breast cancer prognosis","volume":"124","author":"Terunuma","year":"2014","journal-title":"J Clin Invest"},{"key":"2025111209280626400_ref68","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.cmet.2018.09.002","article-title":"PML-regulated mitochondrial metabolism enhances chemosensitivity in human ovarian cancers","volume":"29","author":"Gentric","year":"2019","journal-title":"Cell Metab"},{"key":"2025111209280626400_ref69","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1016\/j.ccell.2021.01.006","article-title":"Proteogenomic and metabolomic characterization of human glioblastoma","volume":"39","author":"Wang","year":"2021","journal-title":"Cancer Cell"},{"key":"2025111209280626400_ref70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-023-02750-7","article-title":"Plasma metabolomics profiling of 580 patients from an early detection research network prostate cancer cohort","volume":"10","author":"Benedetti","year":"2023","journal-title":"Scientific Data"},{"key":"2025111209280626400_ref71","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-024-46043-y","article-title":"Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer. Nature","volume":"15","author":"Chen","year":"2024","journal-title":"Communications"},{"key":"2025111209280626400_ref72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s43856-025-00764-3","article-title":"Urinary metabolite model to predict the dying process in lung cancer patients","volume":"5","author":"Coyle","year":"2025","journal-title":"Commun Med"},{"key":"2025111209280626400_ref73","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1186\/cc2955","article-title":"Statistics review 12: Survival analysis","volume":"8","author":"Bewick","year":"2004","journal-title":"Crit Care"},{"key":"2025111209280626400_ref74","doi-asserted-by":"publisher","first-page":"10546","DOI":"10.1093\/nar\/gky889","article-title":"Multi-omic and multi-view clustering algorithms: Review and cancer benchmark","volume":"46","author":"Rappoport","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2025111209280626400_ref75","doi-asserted-by":"publisher","first-page":"3927","DOI":"10.1002\/sim.2427","article-title":"A time-dependent discrimination index for survival data","volume":"24","author":"Antolini","year":"2005","journal-title":"Stat Med"},{"key":"2025111209280626400_ref76","first-page":"28","article-title":"Package \u2018survival\u2019","volume":"128","author":"Therneau","year":"2015","journal-title":"R Top Doc"},{"key":"2025111209280626400_ref77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-019-2942-y","article-title":"Block forests: Random forests for blocks of clinical and omics covariate data","volume":"20","author":"Hornung","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2025111209280626400_ref78","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa167","article-title":"Large-scale benchmark study of survival prediction methods using multi-omics data","volume":"22","author":"Herrmann","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025111209280626400_ref79","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbaf150","article-title":"A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables","volume":"26","author":"Tran","year":"2025","journal-title":"Brief Bioinform"},{"key":"2025111209280626400_ref80","doi-asserted-by":"publisher","first-page":"W114","DOI":"10.1093\/nar\/gkab421","article-title":"CPA: A web-based platform for consensus pathway analysis and interactive visualization","volume":"49","author":"Nguyen","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2025111209280626400_ref81","doi-asserted-by":"publisher","first-page":"e1036","DOI":"10.1002\/cpz1.1036","article-title":"RCPA: An open-source r package for data processing, differential analysis, consensus pathway analysis, and visualization","volume":"4","author":"Nguyen","year":"2024","journal-title":"Current Protocols"},{"key":"2025111209280626400_ref82","doi-asserted-by":"crossref","first-page":"bbae222","DOI":"10.1093\/bib\/bbae222","article-title":"CCPA: Cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome","volume":"25","author":"Nguyen","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025111209280626400_ref83","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1038\/10343","article-title":"Systematic determination of genetic network architecture","volume":"22","author":"Tavazoie","year":"1999","journal-title":"Nat Genet"},{"key":"2025111209280626400_ref84","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1186\/1476-4598-5-64","article-title":"Pathway analysis of kidney cancer using proteomics and metabolic profiling","volume":"5","author":"Perroud","year":"2006","journal-title":"Mol Cancer"},{"key":"2025111209280626400_ref85","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1214\/07-AOAS101","article-title":"On testing the significance of sets of genes","volume":"1","author":"Efron","year":"2007","journal-title":"The Annals of Applied Statistics"},{"key":"2025111209280626400_ref86","first-page":"060012","article-title":"Fast gene set enrichment analysis","author":"Korotkevich","year":"2016"},{"key":"2025111209280626400_ref87","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1186\/1471-2105-13-136","article-title":"Down-weighting overlapping genes improves gene set analysis","volume":"13","author":"Tarca","year":"2012","journal-title":"BMC Bioinformatics"},{"key":"2025111209280626400_ref88","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.surg.2018.04.080","article-title":"Over expression of DNA damage and cell cycle dependent proteins are associated with poor survival in patients with adrenocortical carcinoma","volume":"165","author":"Subramanian","year":"2019","journal-title":"Surgery"},{"key":"2025111209280626400_ref89","doi-asserted-by":"publisher","first-page":"104","DOI":"10.3390\/biom13010104","article-title":"Identifying immune-specific subtypes of adrenocortical carcinoma based on immunogenomic profiling","volume":"13","author":"Qiqi","year":"2023","journal-title":"Biomolecules"},{"key":"2025111209280626400_ref90","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-73370-z","article-title":"Non-muscle invasive bladder cancer tissues have increased base excision repair capacity","volume":"10","author":"Somuncu","year":"2020","journal-title":"Sci Rep"},{"key":"2025111209280626400_ref91","doi-asserted-by":"publisher","first-page":"101070","DOI":"10.1016\/j.gendis.2023.06.037","article-title":"The role of proteasomes in tumorigenesis","volume":"11","author":"Zhou","year":"2024","journal-title":"Genes & Diseases"},{"key":"2025111209280626400_ref92","doi-asserted-by":"publisher","DOI":"10.1038\/s41419-022-05470-9","article-title":"Non-canonical functions of spliceosome components in cancer progression","volume":"14","author":"Ivanova","year":"2023","journal-title":"Cell Death Dis"},{"key":"2025111209280626400_ref93","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1038\/s41389-018-0044-8","article-title":"The ribosome,(slow) beating heart of cancer (stem) cell","volume":"7","author":"Bastide","year":"2018","journal-title":"Oncogenesis"},{"key":"2025111209280626400_ref94","doi-asserted-by":"publisher","first-page":"2789","DOI":"10.1093\/hmg\/ddw136","article-title":"EZH2 is overexpressed in adrenocortical carcinoma and is associated with disease progression","volume":"25","author":"Drelon","year":"2016","journal-title":"Hum Mol Genet"},{"key":"2025111209280626400_ref95","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1186\/s12943-025-02284-z","article-title":"Regulation of cellular senescence in tumor progression and therapeutic targeting: Mechanisms and pathways","volume":"24","author":"Liu","year":"2025","journal-title":"Mol Cancer"},{"key":"2025111209280626400_ref96","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1016\/j.cell.2015.07.011","article-title":"Inhibiting DNA methylation causes an interferon response in cancer via dsRNA including endogenous retroviruses","volume":"162","author":"Chiappinelli","year":"2015","journal-title":"Cell"},{"key":"2025111209280626400_ref97","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbae185","article-title":"DeepKEGG: A multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery","volume":"25","author":"Lan","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025111209280626400_ref98","doi-asserted-by":"publisher","DOI":"10.1016\/j.fmre.2025.01.004","article-title":"MULGONET: An interpretable neural network framework to integrate multi-omics data for cancer recurrence prediction and biomarker discovery","volume":"20","author":"Lan","year":"2025","journal-title":"Fundamental Research"},{"key":"2025111209280626400_ref99","doi-asserted-by":"publisher","first-page":"11382","DOI":"10.1109\/TNNLS.2023.3260258","article-title":"Multiview subspace clustering via low-rank symmetric affinity graph","volume":"35","author":"Lan","year":"2023","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"2025111209280626400_ref100","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3698192","article-title":"Contrastive clustering learning for multi-behavior recommendation","volume":"43","author":"Lan","year":"2024","journal-title":"ACM Transactions on Information Systems"},{"key":"2025111209280626400_ref101","doi-asserted-by":"publisher","first-page":"4486","DOI":"10.1109\/JBHI.2025.3530794","article-title":"The large language models on biomedical data analysis: A survey","volume":"29","author":"Lan","year":"2025","journal-title":"IEEE J Biomed Health Inform"},{"key":"2025111209280626400_ref102","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: A pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinformatics"},{"key":"2025111209280626400_ref103","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbac409","article-title":"BioGPT: Generative pre-trained transformer for biomedical text generation and mining","volume":"23","author":"Luo","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025111209280626400_ref104","article-title":"ChatDoctor: A medical chat model fine-tuned on a large language model meta-AI (LLaMA) using medical domain knowledge","volume":"15","author":"Li","year":"2023","journal-title":"Cureus"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/6\/bbaf600\/65275908\/bbaf600.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/6\/bbaf600\/65275908\/bbaf600.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:28:18Z","timestamp":1762957698000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf600\/8321764"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"references-count":104,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf600","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"type":"print","value":"1467-5463"},{"type":"electronic","value":"1477-4054"}],"subject":[],"published-other":{"date-parts":[[2025,11]]},"published":{"date-parts":[[2025,11,1]]},"article-number":"bbaf600"}}