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Harnessing Machine Learning and Molecular Docking to Decode the Fatty Acid Dynamics in High-Altitude Yak Milk

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Animals, an international, peer-reviewed Open Access journal.

Summary

This research utilizes machine learning algorithms, such as XGBoost and Random Forest, to predict nutritional components in yak milk based on parity and composition. While it demonstrates the application of AI in agricultural science and molecular docking, the study does not address frontier AI safety, governance, or catastrophic risks. The focus remains strictly on optimizing dairy quality evaluation and biological data interpretation. Therefore, it provides no contribution to the global AI safety policy discourse or the reduction of existential AI risks.

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attachment Supplementary material: Supplementary File 1 (ZIP, 29 KB) 22 pages, 3157 KB   Open AccessArticle Harnessing Machine Learning and Molecular Docking to Decode the Fatty Acid Dynamics in High-Altitude Yak Milk by Chaoyun Yang, Yao Pan, Yi He and Ran Guan Animals 2026, 16(10), 1477; https://doi.org/10.3390/ani16101477 (registering DOI) - 12 May 2026 Abstract This study investigated the fatty acid profile of Muli yak (Bos grunniens) milk and its relationship with compositional parameters across different parities. Milk samples from second-, third-, and fourth-parity yaks were analysed for protein, fat, vitamins, minerals, and 37 fatty acids [...] Read more. This study investigated the fatty acid profile of Muli yak (Bos grunniens) milk and its relationship with compositional parameters across different parities. Milk samples from second-, third-, and fourth-parity yaks were analysed for protein, fat, vitamins, minerals, and 37 fatty acids using gas chromatography. Statistical analyses included ANOVA, correlation analysis, principal component analysis (PCA), machine learning algorithms, and molecular docking. Parity significantly affected 15 components (p < 0.05), with third-parity milk showing the highest eicosapentaenoic acid (EPA, C20:5n3) and arachidonic acid (ARA, C20:4n6) concentrations. Among 134 significant correlations, calcium-ARA and ARA-EPA exhibited strong positive associations (|r| > 0.67). PCA explained 54.2% of the variance through three principal components, differentiating samples by parity. The optimal prediction models were ARA-XGBoost, EPA-Random Forest, ALA-GAM, and LA-SVM, with calcium and protein serving as key predictors. Molecular docking revealed that EPA-FABP2 had the lowest binding energy. These parity-related shifts in functional long-chain polyunsaturated fatty acids are meaningful for the nutritional value of yak milk (e.g., omega-3/omega-6 profile) and may also influence technological properties associated with milk fat composition (e.g., oxidative stability and processing behaviour), supporting parity-oriented quality evaluation and targeted utilisation of yak milk. Full article (This article belongs to the Section Animal System and Management) ►▼ Show Figures Graphical abstract