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Mang Liang

Showing results (1-10 of 19) with videos related to

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Briefings in Bioinformatics|May 8, 2021
KCRR: a nonlinear machine learning with a modified genomic similarity matrix improved the genomic prediction efficiencyBingxing An, Mang Liang, Tianpeng Chang, et al.
Animals : an Open Access Journal From MDPI|September 28, 2021
Genome-Wide Association Study Based on Random Regression Model Reveals Candidate Genes Associated with Longitudinal Data in Chinese Simmental Beef CattleLili Du, Xinghai Duan, Bingxing An, et al.
Journal of Animal Breeding and Genetics = Zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie|October 22, 2020
Application of ensemble learning to genomic selection in chinese simmental beef cattleMang Liang, Jian Miao, Xiaoqiao Wang, et al.
Animals : an Open Access Journal From MDPI|January 20, 2021
Genome-Wide Association Analysis of Growth Curve Parameters in Chinese Simmental Beef CattleXinghai Duan, Bingxing An, Lili Du, et al.
Biology|November 24, 2022
Improving Genomic Prediction with Machine Learning Incorporating TPE for Hyperparameters OptimizationMang Liang, Bingxing An, Keanning Li, et al.
Frontiers in Genetics|March 22, 2021
A Stacking Ensemble Learning Framework for Genomic PredictionMang Liang, Tianpeng Chang, Bingxing An, et al.
Animals : an Open Access Journal From MDPI|June 5, 2019
A Fast and Powerful Empirical Bayes Method for Genome-Wide Association StudiesTianpeng Chang, Julong Wei, Mang Liang, et al.
Scientific Reports|June 8, 2021
Transcriptome profiling analysis of muscle tissue reveals potential candidate genes affecting water holding capacity in Chinese Simmental beef cattleLili Du, Tianpeng Chang, Bingxing An, et al.
Frontiers in Genetics|September 16, 2021
PacBio Single-Molecule Long-Read Sequencing Provides New Light on the Complexity of Full-Length Transcripts in CattleTianpeng Chang, Bingxing An, Mang Liang, et al.
Journal of Animal Science and Biotechnology|September 20, 2022
Incorporating kernelized multi-omics data improves the accuracy of genomic predictionMang Liang, Bingxing An, Tianpeng Chang, et al.
Pageof 2

Showing results (1-10 of 19) with videos related to

Sort By:
Pageof 2
Briefings in Bioinformatics|May 8, 2021
KCRR: a nonlinear machine learning with a modified genomic similarity matrix improved the genomic prediction efficiencyBingxing An, Mang Liang, Tianpeng Chang, et al.
Animals : an Open Access Journal From MDPI|September 28, 2021
Genome-Wide Association Study Based on Random Regression Model Reveals Candidate Genes Associated with Longitudinal Data in Chinese Simmental Beef CattleLili Du, Xinghai Duan, Bingxing An, et al.
Journal of Animal Breeding and Genetics = Zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie|October 22, 2020
Application of ensemble learning to genomic selection in chinese simmental beef cattleMang Liang, Jian Miao, Xiaoqiao Wang, et al.
Animals : an Open Access Journal From MDPI|January 20, 2021
Genome-Wide Association Analysis of Growth Curve Parameters in Chinese Simmental Beef CattleXinghai Duan, Bingxing An, Lili Du, et al.
Biology|November 24, 2022
Improving Genomic Prediction with Machine Learning Incorporating TPE for Hyperparameters OptimizationMang Liang, Bingxing An, Keanning Li, et al.
Frontiers in Genetics|March 22, 2021
A Stacking Ensemble Learning Framework for Genomic PredictionMang Liang, Tianpeng Chang, Bingxing An, et al.
Animals : an Open Access Journal From MDPI|June 5, 2019
A Fast and Powerful Empirical Bayes Method for Genome-Wide Association StudiesTianpeng Chang, Julong Wei, Mang Liang, et al.
Scientific Reports|June 8, 2021
Transcriptome profiling analysis of muscle tissue reveals potential candidate genes affecting water holding capacity in Chinese Simmental beef cattleLili Du, Tianpeng Chang, Bingxing An, et al.
Frontiers in Genetics|September 16, 2021
PacBio Single-Molecule Long-Read Sequencing Provides New Light on the Complexity of Full-Length Transcripts in CattleTianpeng Chang, Bingxing An, Mang Liang, et al.
Journal of Animal Science and Biotechnology|September 20, 2022
Incorporating kernelized multi-omics data improves the accuracy of genomic predictionMang Liang, Bingxing An, Tianpeng Chang, et al.
Pageof 2