{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:55:04Z","timestamp":1773356104603,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Health","award":["R01 LM013061-01"],"award-info":[{"award-number":["R01 LM013061-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Integrating multi-omics data helps identify disease subtypes. Many similarity-based methods were developed for disease subtyping using multi-omics data, with many of them focusing on extracting common clustering structures across multiple types of omics data, but not preserving data-type-specific clustering structures. Moreover, clustering performance of similarity-based methods is affected when similarity measures are noisy. Here we proposed PartIES, a Partition-level Integration using diffusion-Enhanced Similarities to perform disease subtyping using multi-omics data. PartIES uses diffusion to reduce noises in individual similarity\/kernel matrices from individual omics data types first, and then extract partition information from diffusion-enhanced similarity matrices and integrate the partition-level similarity through a weighted average iteratively. Simulation studies showed that (1) the diffusion step enhances clustering accuracy, and (2) PartIES outperforms competing methods, particularly when omics data types provide different clustering structures. Using mRNA, long noncoding RNAs, microRNAs expression data, DNA methylation data, and somatic mutation data from The Cancer Genome Atlas project, PartIES identified subtypes in bladder urothelial carcinoma, liver hepatocellular carcinoma, and thyroid carcinoma that are most significantly associated with patient survival across all methods. Further investigations suggested that among subtype-associated genes, many of those that are highly interacting with other genes are known important cancer genes. The identified cancer subtypes also have different activity levels for some known cancer-related pathways. The R code can be accessed at https:\/\/github.com\/yuqimiao\/PartIES.git<\/jats:p>","DOI":"10.1093\/bib\/bbae609","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T11:46:08Z","timestamp":1732535168000},"source":"Crossref","is-referenced-by-count":5,"title":["PartIES: a disease subtyping framework with Partition-level Integration using diffusion-Enhanced Similarities from multi-omics Data"],"prefix":"10.1093","volume":"26","author":[{"given":"Yuqi","family":"Miao","sequence":"first","affiliation":[{"name":"Department of Biostatistics , Mailman School of Public Health, , New York, NY 10027 ,","place":["United States"]},{"name":"Columbia University , Mailman School of Public Health, , New York, NY 10027 ,","place":["United States"]}]},{"given":"Huang","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics , Mailman School of Public Health, , New York, NY 10027 ,","place":["United States"]},{"name":"Columbia University , Mailman School of Public Health, , New York, NY 10027 ,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1693-6888","authenticated-orcid":false,"given":"Shuang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics , Mailman School of Public Health, , New York, NY 10027 ,","place":["United States"]},{"name":"Columbia University , Mailman School of Public Health, , New York, NY 10027 ,","place":["United States"]}]}],"member":"286","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"key":"2024112511454518900_ref1","doi-asserted-by":"crossref","first-page":"10869","DOI":"10.1073\/pnas.191367098","article-title":"Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications","volume":"98","author":"S\u00f8rlie","year":"2001","journal-title":"Proc Natl Acad Sci"},{"key":"2024112511454518900_ref2","doi-asserted-by":"publisher","first-page":"R36","DOI":"10.1186\/bcr2590","article-title":"Molecular subtypes of breast cancer are associated with characteristic DNA methylation patterns","volume":"12","author":"Holm","year":"2010","journal-title":"Breast Cancer Res"},{"key":"2024112511454518900_ref3","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1038\/s41416-018-0109-7","article-title":"Cancer subtype identification using somatic mutation data","volume":"118","author":"Kuijjer","year":"2018","journal-title":"Br J Cancer"},{"key":"2024112511454518900_ref4","doi-asserted-by":"publisher","first-page":"e1009224","DOI":"10.1371\/journal.pcbi.1009224","article-title":"Evaluation and comparison of multi-omics data integration methods for cancer subtyping","volume":"17","author":"Duan","year":"2021","journal-title":"PLoS Comput Biol"},{"key":"2024112511454518900_ref5","first-page":"252","article-title":"Multi-view clustering via joint nonnegative matrix factorization","volume-title":"Proceedings of the 2013 SIAM International Conference on Data Mining","author":"Liu","year":"2013"},{"key":"2024112511454518900_ref6","doi-asserted-by":"crossref","DOI":"10.1109\/BIBM.2018.8621379","article-title":"Multi-View Factorization AutoEncoder with Network Constraints for Multi-Omic Integrative Analysis","volume-title":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Ma","year":"2018"},{"key":"2024112511454518900_ref7","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":"2024112511454518900_ref8","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":"2024112511454518900_ref9","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":"2024112511454518900_ref10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bioinformatics\/btv544","article-title":"A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data","volume":"32","author":"Yang","year":"2016","journal-title":"Bioinformatics"},{"key":"2024112511454518900_ref11","doi-asserted-by":"publisher","first-page":"2706","DOI":"10.1093\/bioinformatics\/btx176","article-title":"Pattern fusion analysis by adaptive alignment of multiple heterogeneous omics data","volume":"33","author":"Shi","year":"2017","journal-title":"Bioinformatics"},{"key":"2024112511454518900_ref12","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab454","article-title":"A roadmap for multi-omics data integration using deep learning","volume":"23","author":"Kang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024112511454518900_ref13","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":"2024112511454518900_ref14","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":"2024112511454518900_ref15","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1038\/nmeth.4207","article-title":"Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning","volume":"14","author":"Wang","year":"2017","journal-title":"Nat Methods"},{"key":"2024112511454518900_ref16","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":"2024112511454518900_ref17","doi-asserted-by":"publisher","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":"2024112511454518900_ref18","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1186\/s12967-024-04864-x","article-title":"Multi-omics integration with weighted affinity and self-diffusion applied for cancer subtypes identification","volume":"22","author":"Duan","year":"2024","journal-title":"J Transl Med"},{"key":"2024112511454518900_ref19","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1186\/s13058-016-0724-2","article-title":"Expression and methylation patterns partition luminal-a breast tumors into distinct prognostic subgroups","volume":"18","author":"Netanely","year":"2016","journal-title":"Breast Cancer Res"},{"key":"2024112511454518900_ref20","doi-asserted-by":"publisher","first-page":"26001","DOI":"10.1038\/srep26001","article-title":"Molecular subtyping of serous ovarian cancer based on multi-omics data","volume":"6","author":"Zhang","year":"2016","journal-title":"Sci Rep"},{"key":"2024112511454518900_ref21","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":"2024112511454518900_ref22","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.neunet.2019.10.010","article-title":"Partition level multiview subspace clustering","volume":"122","author":"Kang","year":"2020","journal-title":"Neural Netw"},{"key":"2024112511454518900_ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2023.107378","article-title":"MOCSS: multi-omics data clustering and cancer subtyping via shared and specific representation learning","volume":"26","author":"Chen","year":"2023","journal-title":"iScience"},{"key":"2024112511454518900_ref24","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1038\/nrg.2017.38","article-title":"Network propagation: a universal amplifier of genetic associations","volume":"18","author":"Cowen","year":"2017","journal-title":"Nat Rev Genet"},{"key":"2024112511454518900_ref25","doi-asserted-by":"publisher","first-page":"3108","DOI":"10.1038\/s41467-018-05469-x","article-title":"Network enhancement as a general method to denoise weighted biological networks","volume":"9","author":"Wang","year":"2018","journal-title":"Nat Commun"},{"key":"2024112511454518900_ref26","article-title":"Diffusion Improves Graph Learning","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems","author":"Gasteiger","year":"2022"},{"key":"2024112511454518900_ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10302","article-title":"The constrained Laplacian rank algorithm for graph-based clustering","volume":"30","author":"Nie","year":"2016","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"2024112511454518900_ref28","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","article-title":"A tutorial on spectral clustering","volume":"17","author":"von Luxburg","year":"2007","journal-title":"Stat Comput"},{"key":"2024112511454518900_ref29","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/j.cell.2017.09.007","article-title":"Comprehensive molecular characterization of muscle-invasive bladder cancer","volume":"171","author":"Robertson","year":"2017","journal-title":"Cell"},{"key":"2024112511454518900_ref30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s42003-020-01491-2","article-title":"Integrative multi-omics analysis of muscle-invasive bladder cancer identifies prognostic biomarkers for frontline chemotherapy and immunotherapy","volume":"3","author":"Mo","year":"2020","journal-title":"Commun Biol"},{"key":"2024112511454518900_ref31","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1021\/acs.jproteome.8b00702","article-title":"Cytoscape StringApp: network analysis and visualization of proteomics data","volume":"18","author":"Doncheva","year":"2019","journal-title":"J Proteome Res"},{"key":"2024112511454518900_ref32","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1101\/gr.1239303","article-title":"Cytoscape: a software environment for integrated models of biomolecular interaction networks","volume":"13","author":"Shannon","year":"2003","journal-title":"Genome Res"},{"key":"2024112511454518900_ref33","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1515\/biol-2022-0479","article-title":"N6-Methyladenosine-related alternative splicing events play a role in bladder cancer","volume":"17","author":"Chang","year":"2022","journal-title":"Open Life Sci"},{"key":"2024112511454518900_ref34","doi-asserted-by":"publisher","first-page":"353","DOI":"10.7150\/jca.32850","article-title":"Depletion of CDC5L inhibits bladder cancer tumorigenesis","volume":"11","author":"Zhang","year":"2020","journal-title":"J Cancer"},{"key":"2024112511454518900_ref35","doi-asserted-by":"publisher","first-page":"65946","DOI":"10.18632\/oncotarget.19583","article-title":"YBX1 promotes tumor growth by elevating glycolysis in human bladder cancer","volume":"8","author":"Liuyu","year":"2017","journal-title":"Oncotarget"},{"key":"2024112511454518900_ref36","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1038\/s41467-017-02391-6","article-title":"Perturbation-response genes reveal signaling footprints in cancer gene expression","volume":"9","author":"Schubert","year":"2018","journal-title":"Nat Commun"},{"key":"2024112511454518900_ref37","doi-asserted-by":"publisher","first-page":"4587","DOI":"10.1007\/s11033-020-05435-1","article-title":"Role of PI3K\/AKT pathway in cancer: the framework of malignant behavior","volume":"47","author":"Jiang","year":"2020","journal-title":"Mol Biol Rep"},{"key":"2024112511454518900_ref38","article-title":"Impact of alterations affecting the p53 pathway in bladder cancer on clinical outcome, assessed by conventional and Array-based methods","volume-title":"Clinical Cancer Research","author":"Lu"},{"key":"2024112511454518900_ref39","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1186\/s12894-018-0413-9","article-title":"Epidermal growth factor expression as a predictor of chemotherapeutic resistance in muscle-invasive bladder cancer","volume":"18","author":"Mansour","year":"2018","journal-title":"BMC Urol"},{"key":"2024112511454518900_ref40","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1016\/j.cell.2017.05.046","article-title":"Comprehensive and integrative genomic characterization of hepatocellular carcinoma","volume":"169","author":"Wheeler","year":"2017","journal-title":"Cell"},{"key":"2024112511454518900_ref41","doi-asserted-by":"publisher","first-page":"3718","DOI":"10.1093\/bioinformatics\/btz124","article-title":"Using association signal annotations to boost similarity network fusion","volume":"35","author":"Ruan","year":"2019","journal-title":"Bioinformatics"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/1\/bbae609\/60805776\/bbae609.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/1\/bbae609\/60805776\/bbae609.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T11:46:14Z","timestamp":1732535174000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae609\/7908001"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,11,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae609","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,1]]},"published":{"date-parts":[[2024,11,22]]},"article-number":"bbae609"}}