Modeling the temporal evolution of biological systems is fundamental for understanding cellular dynamics and anticipating future functional states. While temporal graph neural networks (TGNNs) have achieved remarkable success in social and financial domains, their evaluation on dynamic biological systems remains largely unexplored. In this work, we provide the first systematic benchmark of discrete-time temporal graph neural networks on dynamic protein-protein interaction (PPI) networks, considering both future link prediction and future gene expression forecasting as complementary structure- and node-level tasks. To capture the recurring and synchronized nature of biological dynamics, we introduce State-Memory Temporal Graph Neural Networks (SM-TGNN), a novel architecture that augments message passing with a compact state-memory mechanism designed to model recurrent structural regimes without relying on sequential recurrent units. Across multiple yeast PPI datasets, SM-TGNN achieves consistently competitive performance in predicting future protein interactions and gene expression profiles, matching or exceeding existing neural approaches across most evaluation settings. At the same time, the strong results obtained by memory-based baselines indicate that temporal link prediction in dynamic biological networks remains a particularly challenging task, requiring models capable of capturing recurrent interaction regimes and long-term temporal dependencies. Notably, a model pre-trained on one PPI network achieves competitive performance when transferred to a distinct yeast cell-cycle dataset, suggesting that the learned state representations capture recurring temporal structures that can partially generalize across related biological settings. Furthermore, SM-TGNN offers competitive inference-time and memory efficiency compared to standard TGNN architectures. Our results demonstrate that state-based temporal modeling provides an effective and scalable inductive bias for learning from dynamic biological networks, opening new directions for temporal graph learning as an AI-driven simulation of cellular processes.
Manuel Dileo, A. Sottoriva· Machine-mediated learning· 0 citations
The interplay between somatic mutations and copy number alterations influences tumor evolution and prognosis. These alterations are often treated independently, overlooking gene mutant dosage (GMD)—a key property of their interaction. Here we develop a computational framework that infers mutation copy number and multiplicity from targeted sequencing panels without requiring matched normal samples. We derive GMD for over 500,000 mutations across 60,000 pan-cancer samples. By stratifying more than 20,000 patients according to GMD across multiple genes, we identify 46 tumor-type-specific biomarkers predictive of survival, 13 of which were undetectable using binary mutant/wild-type models, 26 were associated with metastatic spread and 20 predicted metastatic tropism. Our method reveals GMD patterns as independent predictors of disease prognosis, metastatic potential and site-specific dissemination across diverse tumor types. This augmented insight into genomic drivers enhances our understanding of cancer progression and metastasis and holds the potential to substantially enhance biomarker discovery. The authors present INCOMMON, an open-source Bayesian inference tool that determines the multiplicity and copy number of driver mutations from tumor sequencing datasets.
N. Calonaci, E. Krasniqi, D. Čolić et al.· Nature Genetics· 0 citations
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