Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …

Biomedical data are becoming increasingly multimodal and thereby capture the underlying
complex relationships among biological processes. Deep learning (DL)-based data fusion …

High-throughput next-generation sequencing now makes it possible to generate a vast
amount of multi-omics data for various applications. These data have revolutionized …

The rapid development of precision medicine in recent years has started to challenge
diagnostic pathology with respect to its ability to analyze histological images and …

Computational integrative analysis has become a significant approach in the data-driven
exploration of biological problems. Many integration methods for cancer subtyping have …

Multi-omics allows the systematic understanding of the information flow across different
omics layers, while single omics can mainly reflect one aspect of the biological system. The …

Cancer is the manifestation of abnormalities of different physiological processes involving
genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in …

The lack of explainability is one of the most prominent disadvantages of deep learning
applications in omics. This 'black box'problem can undermine the credibility and limit the …

The increase in the expectations of artificial intelligence (AI) technology has led to machine
learning technology being actively used in the medical field. Non-negative matrix …

Dimensionality reduction approaches are commonly used for the deconvolution of high-
dimensional metabolomics datasets into underlying core metabolic processes. However …