Incorporating kernelized multi-omics data improves the accuracy of genomic prediction

Mang Liang; Bingxing An; Tianpeng Chang; Tianyu Deng; Lili Du; Keanning Li; Sheng Cao; Yueying Du; Lingyang Xu; Lupei Zhang; Xue Gao; Junya Li; Huijiang Gao

doi:10.1186/s40104-022-00756-6

JOURNAL ARTICLE

Incorporating kernelized multi-omics data improves the accuracy of genomic prediction

Mang Liang Bingxing An Tianpeng Chang Tianyu Deng Lili Du Keanning Li Sheng Cao Yueying Du Lingyang Xu Lupei Zhang Xue Gao Junya Li Huijiang Gao

Year: 2022 Journal: Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology Vol: 13 (1)Pages: 103-103 Publisher: BioMed Central

DOI: 10.1186/s40104-022-00756-6

Get Full-Text PDF Get Analytical Report

Keywords:

Omics Computational biology Computer science Data mining Artificial intelligence Machine learning Bioinformatics Biology

Metrics

Cited By

1.27

FWCI (Field Weighted Citation Impact)

Refs

0.78

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Genetic and phenotypic traits in livestock

Life Sciences → Biochemistry, Genetics and Molecular Biology → Genetics

Genetic Mapping and Diversity in Plants and Animals

Life Sciences → Biochemistry, Genetics and Molecular Biology → Genetics

Gene expression and cancer classification

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

Incorporating kernelized multi-omics data improves the accuracy of genomic prediction

Metrics

Citation History

Topics

Related Documents

Multi-omics-data-assisted genomic feature markers preselection improves the accuracy of genomic prediction

Incorporating Omics Data in Genomic Prediction

Additional file 1 of Multi-omics-data-assisted genomic feature markers preselection improves the accuracy of genomic prediction

Additional file 1 of Multi-omics-data-assisted genomic feature markers preselection improves the accuracy of genomic prediction

Genomic prediction powered by multi-omics data