aceIntegrating multi-layer perceptron and random forest in an ensemble framework for improved genomic prediction accuracy and SHAP-derived interpretability of residual feed intake in cattle
Background Feed efficiency (FE) is recognized as a vital component of sustainable dairy production, with residual feed intake (RFI) serving as a key metabolic indicator of FE independent of production levels. However, the genetic improvement of this complex trait is limited by the inability of conventional genomic Best Linear Unbiased Prediction (gBLUP) model to capture complex, non-linear genetic architectures and epistatic interactions. To address these limitations, this study aims to compare the predictive pe…
An ensemble combining Random Forest and Multi-Layer Perceptron improved genomic prediction of residual feed intake in 220 UK Holstein cows to R2=0.39 and RMSE=0.086, outperforming conventional gBLUP.
Findings are exploratory and based on a small cohort of 220 cows, requiring validation in larger populations before application.
Evidence
- Peer-reviewedJournal of Animal Science and Biotechnology2026-08-08
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Truvace Impact Record TRV-2026-0714, v1: “Integrating multi-layer perceptron and random forest in an ensemble framework for improved genomic prediction accuracy and SHAP-derived interpretability of residual feed intake in cattle.” Truvace, 2026-08-09. /record/TRV-2026-0714 (accessed at citation time). sha256 19b5499c4ce2e253…
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