MOFNet integrates multi-omics data using similarity graph pooling and label-space fusion (VCDN), improving cancer subtype classification while retaining interpretable, biologically relevant features.
The main finding – the expression of long non-coding RNAs (in red) – correlates much better with mRNA, as compared to correlation between miRNA (in blue) and mRNA.
This review examines cutting-edge deep learning methods for integrating single-cell multimodal data, highlighting key tools and their applications in harmonizing various omics layers and improving downstream biological analyses.
Integrated multi-omics analyses provide an unprecedented opportunity to better understand the structural and functional properties of microbial communities.
Holo-omics is the use of omics data to study a host and its inherent microbiomes – a biological system known as a “holobiont”.