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A Machine Learning Framework for Genomic Prediction of Paratuberculosis Predisposition in Goats: Discrimination–Calibration Dissociation Across Learning Architectures

2026-04-30

Abstract excerpt

<title>Abstract</title> <p>Genomic prediction of complex disease resistance demands frameworks that jointly optimise discriminative power and probabilistic calibration. We systematically benchmark 14 predictive frameworks — spanning regularised linear models, GBLUP, kernel-based classifiers, tree-based ensembles, deep neural networks, and meta-ensemble strategies — for paratuberculosis predisposition classificati...

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Literature Corpus work
7f40b7cd-dda8-5c4d-b1d6-c4d32051851c
DOI
10.21203/rs.3.rs-9421190/v1
Open publication

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A Machine Learning Framework for Genomic Prediction of Paratuberculosis Predisposition in Goats: Discrimination–Calibration Dissociation Across Learning ArchitecturesDOI 10.21203/rs.3.rs-9421190/v1
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