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Updated: May 10, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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The classification method of donkey breeds based on SNPs data and machine learning.

Dekui Li1,2, Xiaolong Hu2, Yongdong Peng3

  • 1Department of Computer Science, Hubei Water Resources Technical College, Wuhan, China.

Frontiers in Genetics
|April 24, 2025
PubMed
Summary

Accurate donkey breed classification is now possible using machine learning and single nucleotide polymorphism (SNP) data. This genomic approach enhances genetic resource conservation efforts.

Keywords:
LOOCVSMOTESNPsdonkey breed classificationmachine learning

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Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning

Background:

  • Accurate classification of donkey breeds is crucial for genetic resource management and conservation.
  • Genomic data offers a powerful tool for distinguishing between breeds.
  • Existing classification methods may lack precision or scalability.

Purpose of the Study:

  • To develop and validate a machine learning-based method for accurate donkey breed classification using single nucleotide polymorphism (SNP) data.
  • To evaluate the impact of genomic data preprocessing and imbalance handling on classification performance.
  • To identify key genomic regions influencing breed classification accuracy.

Main Methods:

  • Genomic sequencing data preprocessing.
  • Application of Synthetic Minority Over-sampling Technique (SMOTE) for data imbalance.
  • Implementation of improved Leave-One-Out Cross-Validation (LOOCV).
  • Construction and evaluation of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) models.

Main Results:

  • Classifier performance varied significantly across different chromosomes (e.g., Chr2 with KNN, Chr19 with SVM/RF).
  • Data quality enhancement and imbalance correction led to substantial performance improvements.
  • Accuracy, precision, recall, and F1 scores increased by up to 15% in specific models and chromosomes.
  • The developed method demonstrated high efficacy in donkey breed classification.

Conclusions:

  • Integrating SNP data with machine learning provides an effective strategy for donkey breed classification.
  • The method offers valuable technical support for the conservation and development of donkey genetic resources.
  • Further research can explore additional genomic features and advanced machine learning algorithms for improved accuracy.