Microarray Data

Microarray data analysis focuses on extracting meaningful biological information from high-dimensional gene expression datasets, often characterized by a large number of variables and a small number of samples. Current research emphasizes developing robust and efficient classification and clustering algorithms, including adaptations of deep learning (like meta-learning and variational autoencoders), ensemble methods (such as MiniAnDE), and enhanced dimensionality reduction techniques (like PLPCA and IF-PCA), to overcome the challenges posed by this data structure. These advancements improve the accuracy and interpretability of analyses, leading to better diagnostic tools and a deeper understanding of complex biological processes like cancer development. Improved data compression methods also address the substantial storage and processing demands of microarray data.