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Harnessing Machine Learning and Molecular Docking to Decode the Fatty Acid Dynamics in High-Altitude Yak Milk
Chaoyun Yang1, Yao Pan1, Yi He1
1Molecular Breeding Laboratory for Ruminants in Liangshan, Xichang University, Xichang 615000, China.
Animals : an Open Access Journal From MDPI
|May 27, 2026
Summary
Yak milk
Area of Science:
- Animal Science
- Nutritional Biochemistry
- Food Chemistry
Background:
- Yak milk is a valuable source of nutrition.
- Understanding its composition, particularly fatty acids, is crucial.
- Parity (number of calvings) can influence milk quality.
Purpose of the Study:
- To investigate the fatty acid profile of Muli yak milk.
- To explore relationships between fatty acids and other milk components.
- To assess the impact of parity on yak milk composition and functional fatty acids.
Main Methods:
- Gas chromatography for fatty acid analysis.
- Statistical methods: ANOVA, correlation, principal component analysis (PCA).
- Machine learning algorithms (XGBoost, Random Forest, GAM, SVM) and molecular docking.
Main Results:
- Parity significantly affected 15 milk components (p < 0.05).
- Third-parity milk had higher eicosapentaenoic acid (EPA) and arachidonic acid (ARA).
- Strong positive correlations found between calcium-ARA and ARA-EPA; PCA differentiated samples by parity.
Conclusions:
- Parity influences key long-chain polyunsaturated fatty acids (LCPUFAs) in yak milk.
- These shifts impact nutritional value (omega-3/omega-6) and technological properties.
- Supports parity-based quality evaluation and utilization of yak milk.