Akata Z, Thurau C, Bauckhage C. Non-negative matrix factorization in multimodality data for segmentation and label prediction. In: 16th Computer Vision Winter Workshop, Mitterberg, Austria, 2011.
Anderson BW, Kortz MW, Black AC et al. Anatomy, head and neck, skull. In: Kharazi KA (ed.), StatPearls. Treasure Island (FL): StatPearls Publishing, 2024.
Antonelli L, Guarracino MR, Maddalena L et al. Integrating imaging and omics data: a review. Biomed Signal Process Control 2019;52:264–80.
Badea L. Extracting gene expression profiles common to colon and pancreatic adenocarcinoma using simultaneous nonnegative matrix factorization. Pac Symp Biocomput 2008;2008:267–78.
Bodaghi B, Massamba N, Izzedine H. The eye: a window on kidney diseases. Clin Kidney J 2014;7:337–8.
Broadbent C, Song T, Kuang R. Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition. Bioinformatics 2024;40:i529–38.
Cavelaars AE, Kunst AE, Geurts JJ et al. Persistent variations in average height between countries and between socio-economic groups: an overview of 10 European countries. Ann Hum Biol 2000;27:407–21.
Chalise P, Fridley BL. Integrative clustering of multi-level ‘omic data based on non-negative matrix factorization algorithm. Peddada SD (ed.). PLoS One 2017;12:e0176278.
Chauvel C, Novoloaca A, Veyre P et al. Evaluation of integrative clustering methods for the analysis of multi-omics data. Brief Bioinform 2020;21:541–52.
Chen L, Zeng H, Xiang Y et al. Histopathological images and Multi-Omics integration predict molecular characteristics and survival in lung adenocarcinoma. Front Cell Dev Biol 2021;9:720110.
Chen X, Huang JZ, Wu Q et al. Subspace weighting co-clustering of gene expression data. IEEE/ACM Trans Comput Biol Bioinform 2019;16:352–64.
Chiu AM, Mitra M, Boymoushakian L et al. Integrative analysis of the inter-tumoral heterogeneity of triple-negative breast cancer. Sci Rep 2018;8:11807.
Ding C, Li T, Peng W et al. Orthogonal nonnegative matrix t-factorizations for clustering. In: Ungar L (ed.), Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Philadelphia, PA, USA: ACM, 2006, 126–35.
Dissez G, Ceddia G, Pinoli P et al. Drug repositioning predictions by non-negative matrix tri-factorization of integrated association data. In: Shi X, Buck M (eds.), Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. Niagara Falls, NY, USA: ACM, 2019, 25–33.
Ferreira L, Hitchcock DB. A comparison of hierarchical methods for clustering functional data. Commun Stat Simul Comput 2009; 38:1925–49.
Gargano MA, Matentzoglu N, Coleman B et al. The human phenotype ontology in 2024: phenotypes around the world. Nucleic Acids Res 2024;52:D1333–46.
Ghosal A, Nandy A, Das AK et al. A short review on different clustering techniques and their applications. In: Mandal JK, Bhattacharya D (eds), Emerging Technology in Modelling and Graphics. Singapore: Springer Singapore, 2020, 69–83.
He X, Gumbsch T, Roqueiro D et al. Kernel conditional clustering and kernel conditional semi-supervised learning. Knowl Inf Syst 2020; 62:899–925.
Hériché J-K, Alexander S, Ellenberg J. Integrating imaging and omics: computational methods and challenges. Annu Rev Biomed Data Sci 2019;2:175–97.
Khan A, Maji P. Low-rank joint subspace construction for cancer subtype discovery. IEEE/ACM Trans Comput Biol and Bioinf 2020; 17:1290–302.
Kim Y-D, Choi S. Nonnegative tucker decomposition. In: Kanade T, Medioni G (eds.), 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, MN, USA: IEEE, 2007, 1–8.
Kodinariya TM, Makwana PR. Review on determining number of cluster in K-means clustering. Int J Adv Res Comput Sci Manag Stud 2013;1:90–5.
Lee DD, Seung HS. Learning the parts of objects by non-negative matrix factorization. Nature 1999;401:788–91.
Li B, Zhou G, Cichocki A. Two efficient algorithms for approximately orthogonal nonnegative matrix factorization. IEEE Signal Process Lett 2015;22:843–6.
Liu J, Brodley CE, Healy BC et al. Removing confounding factors via constraint-based clustering: an application to finding homogeneous groups of multiple sclerosis patients. Artif Intell Med 2015;65:79–88.
Liu Y, Gu Q, Hou JP et al. A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression. BMC Bioinformatics 2014;15:37.
Loh W-Y, Cao L, Zhou P. Subgroup identification for precision medicine: a comparative review of 13 methods. WIREs Data Min Knowl Discov 2019;9:e1326.
Luo J, Liu Y, Liu P et al. Data integration using tensor decomposition for the prediction of miRNA-Disease associations. IEEE J Biomed Health Inform 2022;26:2370–8.
Malod-Dognin N, Petschnigg J, Windels SFL et al. Towards a data-integrated cell. Nat Commun 2019;10:805.
McLean CY, Bristor D, Hiller M et al. GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol 2010; 28:495–501.
Pourhoseingholi MA, Baghestani AR, Vahedi M. How to control confounding effects by statistical analysis. Gastroenterol Hepatol Bed Bench 2012;5:79–83.
Price AL, Patterson NJ, Plenge RM et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006;38:904–9.
Rappoport N, Shamir R. Multi-omic and multi-view clustering algorithms: review and cancer benchmark. Nucleic Acids Res 2018; 46:10546–62.
Saria S, Goldenberg A. Subtyping: what it is and its role in precision medicine. IEEE Intell Syst 2015;30:70–5.
Schwarz M, Geryk J, Havlovicová M et al. Body mass index is an overlooked confounding factor in existing clustering studies of 3D facial scans of children with autism spectrum disorder. Sci Rep 2024; 14:9873.
Tian C, Kosoy R, Nassir R et al. European population genetic substructure: further definition of ancestry informative markers for distinguishing among diverse European ethnic groups. Mol Med 2009; 15:371–83.
Vijaya Sharma S, Batra N. Comparative study of single linkage, complete linkage, and ward method of agglomerative clustering. In: Yadav D (ed.), 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). Faridabad, India: IEEE, 2019, 568–73.
Wang B, Mezlini AM, Demir F et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods 2014; 11:333–7.
Wang R-S, Maron BA, Loscalzo J. Multiomics network medicine approaches to precision medicine and therapeutics in cardiovascular diseases. Arterioscler Thromb Vasc Biol 2023;43:493–503.
Wang X, Sun Z, Zhang Y et al. BREM-SC: a Bayesian random effects mixture model for joint clustering single cell multi-omics data. Nucleic Acids Res 2020;48:5814–24.
White JD, Indencleef K, Naqvi S et al. Insights into the genetic architecture of the human face. Nat Genet 2021;53:45–53.
Yun Y, Xia W, Zhang Y et al. Self-representation and class-specificity distribution based multi-view clustering. Neurocomputing 2021; 437:9–20.
Zhang S, Liu C-C, Li W et al. Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Res 2012;40:9379–91.
