Cancer is a collection of genetic diseases, with large phenotypic differences and genetic
heterogeneity between different types of cancers and even within the same cancer type …

Three-dimensional data are increasingly prevalent across biomedical and social domains.
Notable examples are gene-sample-time, individual-feature-time, or node-node-time data …

Tensor clustering with planted structures: Statistical optimality and computational limits
Page 1 The Annals of Statistics 2022, Vol. 50, No. 1, 584–613 https://doi.org/10.1214/21-AOS2123 …

Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved
its effectiveness in bioinformatics due to its capacity to produce local instead of global …

Statistical evaluation of biclustering solutions is essential to guarantee the absence of
spurious relations and to validate the high number of scientific statements inferred from …

Computational lower bounds for graphon estimation via low-degree polynomials Page 1 The
Annals of Statistics 2024, Vol. 52, No. 5, 2318–2348 https://doi.org/10.1214/24-AOS2437 © …

The analysis of temporal data has gained increasing attention in recent years, aiming to
identify patterns and trends that change over time. Temporal triclustering is a promising …

A model-based biclustering method for multivariate discrete longitudinal data is proposed.
We consider a finite mixture of generalized linear models to cluster units and, within each …

Motivation: Detecting modules of co-ordinated activity is fundamental in the analysis of large
biological studies. For two-dimensional data (eg genes× patients), this is often done via …

Tensor data analysis allows researchers to uncover novel patterns and relationships that
cannot be obtained from tabular data alone. The information inferred from multi-way patterns …