A phenotype-driven framework integrates GNNs, causal inference, probabilistic reasoning, and LLMs to expand knowledge graphs via multi-objective optimization that balances novelty, relevance, and evidence validation.
DAGSLAM: causal Bayesian network structure learning of mixed type data and its application in identifying disease risk factors
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A phenotype-driven and evidence-governed framework for knowledge graph enrichment and hypotheses discovery in population data
A phenotype-driven framework integrates GNNs, causal inference, probabilistic reasoning, and LLMs to expand knowledge graphs via multi-objective optimization that balances novelty, relevance, and evidence validation.