Dennis Veselkov

Kyle Higgins, Guadalupe Gonzalez, Dennis Veselkov et al.

Understanding how molecular alterations propagate across biological systems to drive disease remains a central challenge. Although high-throughput profiling enables comprehensive characterization of tumor states, most models neglect structured biological relationships or lack interpretability across scales. Here we present PPI-Net, a hierarchical graph neural network that integrates protein-protein interaction (PPI) networks with pathway-level representations to model disease from molecular interactions to functional processes. Patient-specific molecular profiles are embedded within a shared interaction network from STRING and propagated through a multi-layer Reactome hierarchy using graph attention, enabling aggregation of gene-level signals into higher-order biological programs. Across RNA-seq data from ten cancer types from The Cancer Genome Atlas, PPI-Net achieves robust predictive performance, with balanced accuracy exceeding 90% in multiple cohorts. Comparative analysis on RNA-Seq data from breast cancer demonstrated that PPI-Net's integration of the Reactome hierarchy improved balanced accuracy by 6.7% relative to a PPI-only model, while hierarchical multi-level supervision improved balanced accuracy by 12.3% relative to using only a single top-level prediction head. Applying a multi-omics approach using RNA-seq and methylation data improves model interpretation, recovering canonical oncogenic modules, including TP53-AKT signaling and stress response pathways, while revealing convergence onto coherent programs such as ion signaling and cellular responses to stimuli. These results demonstrate that integrating interaction networks with pathway hierarchies enables accurate prediction while providing mechanistic insight into cancer biology.

Kyle Higgins, Ivan Laponogov, Dennis Veselkov et al.

Graph neural networks (GNNs) are increasingly used to model biological systems, yet the reliability of post-hoc explanation methods for recovering meaningful molecular mechanisms remains unclear. Here, we systematically evaluate four widely used approaches: Saliency Attribution (SA), Integrated Gradients (IG), GNNExplainer, and Layer-wise Relevance Propagation (LRP) for identifying disease-relevant structure in breast cancer RNA-seq data projected onto a protein-protein interaction network. Using synthetic benchmarks with known ground-truth motifs, we show that explanation methods recover distinct signal organizations: SA performs best for sparse single-node drivers, whereas IG and LRP preferentially recover distributed pathway-like and cascade-like signals. In TCGA BRCA data, we identify a consistent topological signature of disease-associated hubs in which attribution peaks in the immediate 1-hop neighborhood and decays across successive network shells, a pattern most pronounced for IG and LRP and associated with strong enrichment of known cancer hubs. We further observe a trade-off between local hub enrichment and global gene ranking performance, with IG optimizing local enrichment and SA achieving superior global discrimination. Motivated by these complementary behaviors, we introduce a framework combining a shell-based hub score with consensus ranking across explainers. Consensus scores improve prioritization of canonical cancer genes (TP53, BRCA1, ESR1, MYC), reduce dependence on node degree, and, especially when tuned, outperform individual methods. Pathway enrichment further reveals improved recovery of biologically coherent cancer programs, including ERBB2, RTK, MAPK, immune, and cytokine signaling. Together, these results demonstrate that topology-aware integration of graph explanations can improve biological interpretability and biologically relevant molecular recovery.

Kyle Higgins, Olga P. Nyssen, Joshua Southern et al.

Helicobacter pylori (H. pylori) is the most common carcinogenic pathogen worldwide. Infecting roughly 1 in 2 individuals globally, it is the leading cause of peptic ulcer disease, chronic gastritis, and gastric cancer. To investigate whether personalized treatments would be optimal for patients suffering from infection, we developed the H. pylori AI-clinician recommendation system. This system was trained on data from tens of thousands of H. pylori-infected patients from Hp-EuReg, orders of magnitude greater than those experienced by a single real-world clinician. We first used a simulated dataset and demonstrated the ability of our AI Clinician method to identify patient subgroups that would benefit from differential optimal treatments. Next, we trained the AI Clinician on Hp-EuReg, demonstrating the AI Clinician reproduces known quality estimates of treatments, for example bismuth and quadruple therapies out-performing triple, with longer durations and higher dose proton pump inhibitor (PPI) showing higher quality estimation on average. Next we demonstrated that treatment was optimized by recommended personalized therapies in patient subsets, where 65% of patients were recommended a bismuth therapy of either metronidazole, tetracycline, and bismuth salts with PPI, or bismuth quadruple therapy with clarithromycin, amoxicillin, and bismuth salts with PPI, and 15% of patients recommended a quadruple non-bismuth therapy of clarithromycin, amoxicillin, and metronidazole with PPI. Finally, we determined trends in patient variables driving the personalized recommendations using random forest modelling. With around half of the world likely to experience H. pylori infection at some point in their lives, the identification of personalized optimal treatments will be crucial in both gastric cancer prevention and quality of life improvements for countless individuals worldwide.

Hamideh Kerdegari, Kyle Higgins, Dennis Veselkov et al.

The integration of artificial intelligence (AI) in medical diagnostics represents a significant advancement in managing upper gastrointestinal (GI) cancer, a major cause of global cancer mortality. Specifically for gastric cancer (GC), chronic inflammation causes changes in the mucosa such as atrophy, intestinal metaplasia (IM), dysplasia and ultimately cancer. Early detection through endoscopic regular surveillance is essential for better outcomes. Foundation models (FM), which are machine or deep learning models trained on diverse data and applicable to broad use cases, offer a promising solution to enhance the accuracy of endoscopy and its subsequent pathology image analysis. This review explores the recent advancements, applications, and challenges associated with FM in endoscopy and pathology imaging. We started by elucidating the core principles and architectures underlying these models, including their training methodologies and the pivotal role of large-scale data in developing their predictive capabilities. Moreover, this work discusses emerging trends and future research directions, emphasizing the integration of multimodal data, the development of more robust and equitable models, and the potential for real-time diagnostic support. This review aims to provide a roadmap for researchers and practitioners in navigating the complexities of incorporating FM into clinical practice for prevention/management of GC cases, thereby improving patient outcomes.