Semi-supervised learning for genomic prediction of novel traits with small reference populations: an application to residual feed intake in dairy cattle.

Journal: Genetics, selection, evolution : GSE
PMID: 27821057

Abstract

BACKGROUND: Genomic prediction for novel traits, which can be costly and labor-intensive to measure, is often hampered by low accuracy due to the limited size of the reference population. As an option to improve prediction accuracy, we introduced a semi-supervised learning strategy known as the self-training model, and applied this method to genomic prediction of residual feed intake (RFI) in dairy cattle.

Authors

  • Chen Yao
    Division of Vascular Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510800, China; National-Guangdong Joint Engineering Laboratory for Diagnosis and Treatment of Vascular Disease, First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China. Electronic address: yaochen@mail.sysu.edu.cn.
  • Xiaojin Zhu
    Department of Computer Science, University of Wisconsin, Madison, Madison, WI, USA.
  • Kent A Weigel
    Department of Dairy Science, University of Wisconsin, Madison, Madison, WI, USA.

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