TL;DR
Key results support a general conclusion: feature-graph guidance can improve small-sample multivariate quality assessment when the supplied structure is outcome-relevant, but graph relevance must be tested rather than assumed.
The proposed engGNN is highlighted as a robust, flexible, and interpretable framework for disease classification and biomarker discovery in high-dimensional omics contexts and provides interpretable feature importance scores that facilitate biologically meaningful discoveries, such as pathway enrichment analysis.
Untargeted urinary metabolomics represents a promising approach for investigating metabolic alterations associated with oncogenic processes such as breast cancer (BC). However, the stable selection of informative m/z features remains a central challenge in biomarker-oriented studies, particularly in the context of earl...
MultiSpaceNet is presented, a graph-based framework for joint representation learning from paired spatial transcriptomic and proteomic data that outperforms nine published state-of-the-art methods in spatial domain identification and preserves biological structure across replicate sections better than all compared alte...
Biomarker discovery from high-dimensional RNA sequencing data remains challenging. Conventional methods such as differential expression analysis, pairwise protein-protein interaction networks, and weighted gene co-expression network analysis suffer from false-positive interactions, transitivity-driven noise, and arbitr...
The performance of target-specific, ligand-based virtual screening models is strongly influenced by dataset characteristics, including data availability, class imbalance, and evaluation strategies. In this work, we perform a systematic evaluation of graph neural networks (GNNs) using a ChEMBL-derived dataset spanning...
Results support graph-connected gene blocks as useful prediction units for JEPA-style representation learning in single-cell biology by supporting block-level prediction of graph-connected gene blocks defined by protein-association and corpus-derived coexpression evidence.
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