2025/03/18 by Andrea Zerio, Zerio, Andrea, Maya Bechler-Speicher +5 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · Health Professions · #Artificial Intelligence in Healthcare #Digital Imaging for Blood Diseases #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Quantitative Methods (q-bio.QM) #cs.LG #q-bio.QM
paper · pdf · doi:10.48550/arxiv.2503.14561
openalex publication_date 2025/03/18 · arxiv published 2025/03/18 · arxiv updated 2025/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Routinely collected clinical blood tests are an emerging molecular data source for large-scale biomedical research but inherently feature irregular sampling and informative observation. Traditional approaches rely on imputation, which can distort learning signals and bias predictions while lacking biological interpretability. We propose a novel methodology using Graph Neural Additive Networks (GNAN) to model biomarker trajectories as time-weighted directed graphs, where nodes represent sampling events and edges encode the time delta between events. GNAN's additive structure enables the explicit decomposition of feature and temporal contributions, allowing the detection of critical disease-associated time points. Unlike conventional imputation-based approaches, our method preserves the temporal structure of sparse data without introducing artificial biases and provides inherently interpretable predictions by decomposing contributions from each biomarker and time interval. This makes our model clinically applicable, as well as allowing it to discover biologically meaningful disease signatures.