Mang Liang

445 total citations
19 papers, 258 citations indexed

About

Mang Liang is a scholar working on Genetics, Cancer Research and Animal Science and Zoology. According to data from OpenAlex, Mang Liang has authored 19 papers receiving a total of 258 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Genetics, 10 papers in Cancer Research and 5 papers in Animal Science and Zoology. Recurrent topics in Mang Liang's work include Genetic and phenotypic traits in livestock (18 papers), Genetic Mapping and Diversity in Plants and Animals (14 papers) and Cancer-related molecular mechanisms research (10 papers). Mang Liang is often cited by papers focused on Genetic and phenotypic traits in livestock (18 papers), Genetic Mapping and Diversity in Plants and Animals (14 papers) and Cancer-related molecular mechanisms research (10 papers). Mang Liang collaborates with scholars based in China. Mang Liang's co-authors include Huijiang Gao, Junya Li, Lingyang Xu, Lupei Zhang, Bingxing An, Lili Du, Tianpeng Chang, Xue Gao, Xue Gao and Jian Miao and has published in prestigious journals such as PLoS ONE, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Mang Liang

19 papers receiving 253 citations

Peers

Mang Liang

GENET

ASZ

Bingxing An China

Aisaku Arakawa Japan

Jian Miao China

Tianpeng Chang China

Shaokang Chen China

Mohammad Ferdosi Australia

Tianyu Deng China

Juliana Petrini Brazil

Iwona Szatkowska Poland

Hongmei Gao China

Bingxing An China

Mang Liang

269 ×1.7

GENET

83 ×1.5

ASZ

101 ×1.8

61 ×1.4

48 ×1.5

Citations per year, relative to Mang Liang Mang Liang (= 1×) peers Bingxing An

Countries citing papers authored by Mang Liang

Since Specialization

Citations

This map shows the geographic impact of Mang Liang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mang Liang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mang Liang more than expected).

Fields of papers citing papers by Mang Liang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mang Liang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mang Liang. The network helps show where Mang Liang may publish in the future.

Co-authorship network of co-authors of Mang Liang

This figure shows the co-authorship network connecting the top 25 collaborators of Mang Liang. A scholar is included among the top collaborators of Mang Liang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mang Liang. Mang Liang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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19 of 19 papers shown

Du, Lili, Tianyu Deng, Mang Liang, et al.. (2025). Integrating genomics and transcriptomics reveals candidate genes affecting loin muscle area in Huaxi cattle. PLoS ONE. 20(5). e0322026–e0322026. 1 indexed citations

Wang, Jinbu, Jia Zhang, Mang Liang, et al.. (2025). An interpretable integrated machine learning framework for genomic selection. Smart Agricultural Technology. 12. 101138–101138. 1 indexed citations

Deng, Tianyu, Mang Liang, Lili Du, et al.. (2024). Transcriptome Analysis of Compensatory Growth and Meat Quality Alteration after Varied Restricted Feeding Conditions in Beef Cattle. International Journal of Molecular Sciences. 25(5). 2704–2704. 4 indexed citations

Deng, Tianyu, Lili Du, Mang Liang, et al.. (2024). Genome-Wide Gene–Environment Interaction Analysis Identifies Novel Candidate Variants for Growth Traits in Beef Cattle. Animals. 14(11). 1695–1695. 1 indexed citations

Liang, Mang, Bingxing An, Tianyu Deng, et al.. (2023). Incorporating genome-wide and transcriptome-wide association studies to identify genetic elements of longissimus dorsi muscle in Huaxi cattle. Frontiers in Genetics. 13. 982433–982433. 6 indexed citations

An, Bingxing, Mang Liang, Tianpeng Chang, et al.. (2023). Prescreening of large-effect markers with multiple strategies improves the accuracy of genomic prediction. Journal of Integrative Agriculture. 23(5). 1634–1643. 2 indexed citations

Liang, Mang, Sheng Cao, Tianyu Deng, et al.. (2023). MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits. Briefings in Bioinformatics. 24(2). 7 indexed citations

Liang, Mang, Bingxing An, Tianpeng Chang, et al.. (2022). Incorporating kernelized multi-omics data improves the accuracy of genomic prediction. Journal of Animal Science and Biotechnology. 13(1). 103–103. 2 indexed citations

Du, Lili, Tianpeng Chang, Bingxing An, et al.. (2022). Integrating genomics and transcriptomics to identify candidate genes for subcutaneous fat deposition in beef cattle. Genomics. 114(4). 110406–110406. 24 indexed citations

10.

Du, Lili, Tianpeng Chang, Bingxing An, et al.. (2022). Transcriptomics and Lipid Metabolomics Analysis of Subcutaneous, Visceral, and Abdominal Adipose Tissues of Beef Cattle. Genes. 14(1). 37–37. 18 indexed citations

11.

Liang, Mang, Bingxing An, Lili Du, et al.. (2022). Improving Genomic Prediction with Machine Learning Incorporating TPE for Hyperparameters Optimization. Biology. 11(11). 1647–1647. 14 indexed citations

12.

Liang, Mang, Tianpeng Chang, Bingxing An, et al.. (2021). A Stacking Ensemble Learning Framework for Genomic Prediction. Frontiers in Genetics. 12. 600040–600040. 60 indexed citations

13.

Chang, Tianpeng, Bingxing An, Mang Liang, et al.. (2021). PacBio Single-Molecule Long-Read Sequencing Provides New Light on the Complexity of Full-Length Transcripts in Cattle. Frontiers in Genetics. 12. 664974–664974. 12 indexed citations

14.

Du, Lili, Tianpeng Chang, Bingxing An, et al.. (2021). Transcriptome profiling analysis of muscle tissue reveals potential candidate genes affecting water holding capacity in Chinese Simmental beef cattle. Scientific Reports. 11(1). 11897–11897. 14 indexed citations

15.

An, Bingxing, Mang Liang, Tianpeng Chang, et al.. (2021). KCRR: a nonlinear machine learning with a modified genomic similarity matrix improved the genomic prediction efficiency. Briefings in Bioinformatics. 22(6). 22 indexed citations

16.

Du, Lili, Bingxing An, Tianpeng Chang, et al.. (2021). Genome-Wide Association Study Based on Random Regression Model Reveals Candidate Genes Associated with Longitudinal Data in Chinese Simmental Beef Cattle. Animals. 11(9). 2524–2524. 13 indexed citations

17.

An, Bingxing, Lili Du, Tianpeng Chang, et al.. (2021). Genome-Wide Association Analysis of Growth Curve Parameters in Chinese Simmental Beef Cattle. Animals. 11(1). 192–192. 33 indexed citations

18.

Liang, Mang, Jian Miao, Xiaoqiao Wang, et al.. (2020). Application of ensemble learning to genomic selection in chinese simmental beef cattle. Journal of Animal Breeding and Genetics. 138(3). 291–299. 22 indexed citations

19.

Chang, Tianpeng, Julong Wei, Mang Liang, et al.. (2019). A Fast and Powerful Empirical Bayes Method for Genome-Wide Association Studies. Animals. 9(6). 305–305. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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