Biclustering algorithms represent a key methodological advance in analysing gene expression data, enabling simultaneous clustering of both genes and experimental conditions. This dual clustering ...

Using whole-slide hematoxylin and eosin images from 214 patients with glioblastoma in The Cancer Genome Atlas (TCGA), a fine-tuned convolutional neural network model extracted deep learning features.

Triclustering algorithms offer a sophisticated approach to analysing gene expression data by simultaneously grouping genes, experimental conditions, and temporal points. These multidimensional methods ...