Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data
Li Z, Windels S, Malod-Dognin N, Weinberg S, Marazita M, Walsh S, Shriver M, Fardo D, Claes P, Pržulj N, Van Steen K. Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data. Bioinformatics 2025, 41: btaf122. PMID: 40119919, PMCID: PMC11978392, DOI: 10.1093/bioinformatics/btaf122.Peer-Reviewed Original ResearchConceptsNonnegative matrix tri-factorizationMulti-view clustering methodsClustering methodMulti-view clusteringLow-rank embeddingDrivers of clusteringMatrix tri-factorizationBiologically meaningful interpretationSingle-view approachImage dataAdjusted Rand IndexEmbedding vectorsEmbedding frameworkFacial annotationsOmics dataSynthetic datasetsTri-factorizationRelevant embeddingsRand indexClusters of individualsOmicsComprehensive clusteringEmbeddingCluster individualsExternal quality
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