{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T10:50:20Z","timestamp":1791715820564,"version":"4.3.4"},"reference-count":114,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T00:00:00Z","timestamp":1791676800000},"content-version":"vor","delay-in-days":40,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PNRR-PE-AI FAIR"},{"name":"Next Generation EU program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The interpretation of genetic variants remains central to precision medicine, and machine-learning-based variant effect prediction tools have become indispensable for supporting decision-making processes. In this review we trace the progression from hand-crafted features and traditional machine learning classifiers, through supervised ensembles, to deep-learning architectures and foundation models that learn representations directly from biological sequences. We classify methods for genetic variant effect prediction by underlying machine learning model and variant type handled, from missense to non-coding and structural, as well as across feature categories, describing their biological rationale, adoption rate, and known limitations. We further examine three cross-cutting methodological challenges\u2014feature orthogonality, data circularity, and training data bias\u2014that affect reliability and clinical applicability, providing a critical overview of the current landscape and the challenges that lie ahead.<\/jats:p>","DOI":"10.1093\/bib\/bbag552","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T11:26:53Z","timestamp":1790335613000},"source":"Crossref","is-referenced-by-count":0,"title":["From conservation scores to language models: a feature engineering perspective on machine learning for genetic variant interpretation"],"prefix":"10.1093","volume":"27","author":[{"given":"Matteo","family":"Bulloni","sequence":"first","affiliation":[{"name":"Department of Electronics, Information and Bioengineering, Politecnico di Milano , Piazza Leonardo da Vinci 32, 20133 Milan 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