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Epigenetic subclassification of meningiomas based on genome-wide DNA methylation analyses

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MOGSA: Integrative Single Sample Gene-set Analysis of Multiple Omics Data

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TbCAPs: A toolbox for co-activation pattern analysis

Bolton, T. A. W., Tuleasca, C., Wotruba, D., Rey, G., Dhanis, H., Gauthier, B., Delavari, F., Morgenroth, E., Gaviria, J., Blondiaux, E., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116621 · Cited by 77

Optimality of spectral clustering in the Gaussian mixture model

Löffler, M., Zhang, A. Y., & Zhou, H. H. (2021) · The Annals of Statistics · doi:10.1214/20-aos2044 · Cited by 77

Unsupervised class discovery in pancreatic ductal adenocarcinoma reveals cell-intrinsic mesenchymal features and high concordance between existing classification systems

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Combining Multiple Clustering Systems

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Learning States and Rules for Time Series Anomaly Detection.

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Characterizing the functional connectivity diversity of the insula cortex: Subregions, diversity curves and behavior

Tian, Y., & Zalesky, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.055 · Cited by 80

Consensus Coexpression Network Analysis Identifies Key Regulators of Flower and Fruit Development in Wild Strawberry

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Mapping functional to morphological variation reveals the basis of regional extracellular matrix subversion and nerve invasion in pancreatic cancer

Chiaro, P. D., Nacci, L., Arco, F., Brandini, S., Polletti, S., Palamidessi, A., Donati, B., Soriani, C., Gualdrini, F., Frigè, G., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.02.017 · Cited by 80

Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets

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Enrichment of longevity phenotype in mtDNA haplogroups D4b2b, D4a, and D5 in the Japanese population

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Proteogenomics of glioblastoma associates molecular patterns with survival

Arad, G., Ofek, P., Yeini, E., Mardamshina, M., Danilevsky, A., Shomron, N., Grossman, R., Satchi‐Fainaro, R., & Geiger, T. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108787 · Cited by 78

Ensemble clustering method based on the resampling similarity measure for gene expression data

Kim, S. C., & Lee, J. W. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206071842 · Cited by 16

Consensus Clustering for Microarray Gene Expression Data

Muthukalathi, S., Ramanujam, R., & Thalamuthu, A. (2014) · Bonfring International Journal of Data Mining · doi:10.9756/bijdm.6140 · Cited by 3

Consensus Clustering in Gene Expression

Galdi, P., Napolitano, F., & Tagliaferri, R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24462-4_5 · Cited by 11

Consensus Clustering for Time Course Gene Expression Microarray Data

Kim, S., & Bae, J. S. (2005) · Communications for Statistical Applications and Methods · doi:10.5351/ckss.2005.12.2.335

Analysis of gene expression data: clustering and beyond

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