Brief

Researchers have developed a novel deep learning framework called MOGKAN to classify multi-omics data for cancer diagnostics. This framework integrates messenger-RNA, micro-RNA, and DNA methylation samples with protein-protein interaction networks. MOGKAN achieves a 96.28% classification accuracy and offers enhanced interpretability through trainable univariate functions, identifying biomarkers validated by gene ontology analysis. AI

Researchers have introduced DOGMA, a novel data-centric AI framework for single-cell transcriptomics analysis. This framework integrates multi-level biological prior knowledge, moving beyond purely data-driven heuristics. DOGMA constructs biologically grounded cell graphs by incorporating statistical alignment with Cell Ontology and phylogenetic structure, and enhances feature-level semantics using Gene Ontology. The system demonstrates robustness in zero-shot cell-type evaluation and sample efficiency across complex multi-species benchmarks, while requiring less GPU memory and inference time. AI