Identifying Critical Phases for Disease Onset with Sparse Haematological Biomarkers
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908274309201920 |
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| author | Zerio, Andrea Bechler-Speicher, Maya Jess, Tine Sazonovs, Aleksejs |
| author_facet | Zerio, Andrea Bechler-Speicher, Maya Jess, Tine Sazonovs, Aleksejs |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14561 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identifying Critical Phases for Disease Onset with Sparse Haematological Biomarkers Zerio, Andrea Bechler-Speicher, Maya Jess, Tine Sazonovs, Aleksejs Quantitative Methods Machine Learning 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. |
| title | Identifying Critical Phases for Disease Onset with Sparse Haematological Biomarkers |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2503.14561 |