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arXiv · 2505.13534

InterFeat: A Pipeline for Finding Interesting Scientific Features

Dan Ofer·Michal Linial·Dafna Shahaf
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Abstract

Finding interesting phenomena is the core of scientific discovery, but it is a manual, ill-defined concept. We present an integrative pipeline for automating the discovery of interesting simple hypotheses (feature-target relations with effect direction and a potential underlying mechanism) in structured biomedical data. The pipeline combines machine learning, knowledge graphs, literature search and Large Language Models. We formalize "interestingness" as a combination of novelty, utility and plausibility. On 8 major diseases from the UK Biobank, our pipeline consistently recovers risk factors years before their appearance in the literature. 40--53% of our top candidates were validated as interesting, compared to 0--7% for a SHAP-based baseline. Overall, 28% of 109 candidates were interesting to medical experts. The pipeline addresses the challenge of operationalizing "interestingness" scalably and for any target. We release data and code: https://github.com/LinialLab/InterFeat

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Dan Ofer, Michal Linial, Dafna Shahaf. 2025-05-18. InterFeat: A Pipeline for Finding Interesting Scientific Features. https://arxiv.org/abs/2505.13534

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