Jinling Meng

2.9k total citations
52 papers, 2.2k citations indexed

About

Jinling Meng is a scholar working on Plant Science, Molecular Biology and Genetics. According to data from OpenAlex, Jinling Meng has authored 52 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Plant Science, 40 papers in Molecular Biology and 16 papers in Genetics. Recurrent topics in Jinling Meng's work include Chromosomal and Genetic Variations (19 papers), Nitrogen and Sulfur Effects on Brassica (18 papers) and Plant Disease Resistance and Genetics (18 papers). Jinling Meng is often cited by papers focused on Chromosomal and Genetic Variations (19 papers), Nitrogen and Sulfur Effects on Brassica (18 papers) and Plant Disease Resistance and Genetics (18 papers). Jinling Meng collaborates with scholars based in China, Australia and Germany. Jinling Meng's co-authors include Long Yan, Ian Bancroft, Martin Trick, Jun Zou, Chunyu Zhang, Maoteng Li, Wei Qian, Yan Long, Graham J.W. King and Jiaqin Shi and has published in prestigious journals such as PLoS ONE, Scientific Reports and New Phytologist.

In The Last Decade

Jinling Meng

52 papers receiving 2.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jinling Meng China 28 1.7k 1.5k 530 387 62 52 2.2k
Wolfgang Ecke Germany 17 1.0k 0.6× 901 0.6× 554 1.0× 426 1.1× 33 0.5× 28 1.4k
Liezhao Liu China 24 1.3k 0.7× 998 0.7× 293 0.6× 267 0.7× 73 1.2× 67 1.6k
Akshay K. Pradhan India 26 1.4k 0.8× 1.3k 0.9× 339 0.6× 209 0.5× 22 0.4× 65 1.8k
Chaozhi Ma China 29 1.6k 0.9× 1.8k 1.2× 262 0.5× 265 0.7× 28 0.5× 95 2.2k
Jinxing Tu China 34 2.8k 1.7× 2.9k 2.0× 536 1.0× 478 1.2× 72 1.2× 166 3.8k
Takuichi Fuse Japan 10 2.4k 1.4× 1.2k 0.8× 1.2k 2.2× 224 0.6× 84 1.4× 12 2.6k
Jinxiong Shen China 33 2.6k 1.5× 2.7k 1.8× 372 0.7× 405 1.0× 60 1.0× 176 3.5k
Jiaqin Shi China 20 1.2k 0.7× 949 0.6× 493 0.9× 389 1.0× 57 0.9× 34 1.5k
Xueli An China 25 1.6k 0.9× 1.2k 0.8× 164 0.3× 101 0.3× 67 1.1× 49 1.9k
Jinling Meng China 24 1.9k 1.1× 1.2k 0.8× 577 1.1× 318 0.8× 124 2.0× 39 2.3k

Countries citing papers authored by Jinling Meng

Since Specialization
Citations

This map shows the geographic impact of Jinling Meng'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 Jinling Meng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jinling Meng more than expected).

Fields of papers citing papers by Jinling Meng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jinling Meng. 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 Jinling Meng. The network helps show where Jinling Meng may publish in the future.

Co-authorship network of co-authors of Jinling Meng

This figure shows the co-authorship network connecting the top 25 collaborators of Jinling Meng. A scholar is included among the top collaborators of Jinling Meng 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 Jinling Meng. Jinling Meng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hu, Dandan, Rod J. Snowdon, Annaliese S. Mason, et al.. (2021). Exploring the gene pool ofBrassica napusby genomics‐based approaches. Plant Biotechnology Journal. 19(9). 1693–1712. 44 indexed citations
2.
Hu, Dandan, Yikai Zhang, Yingying Chen, et al.. (2018). Reconstituting the genome of a young allopolyploid crop, Brassica napus, with its related species. Plant Biotechnology Journal. 17(6). 1106–1118. 18 indexed citations
3.
Bayer, Philipp E., Bhavna Hurgobin, Agnieszka A. Golicz, et al.. (2017). Assembly and comparison of two closely related Brassica napus genomes. Plant Biotechnology Journal. 15(12). 1602–1610. 104 indexed citations
4.
Luo, Ziliang, Meng Wang, Yan Long, et al.. (2017). Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example. Theoretical and Applied Genetics. 130(8). 1569–1585. 64 indexed citations
5.
Long, Yan, Nian Wang, Jun Zou, et al.. (2017). Breeding histories and selection criteria for oilseed rape in Europe and China identified by genome wide pedigree dissection. Scientific Reports. 7(1). 1916–1916. 15 indexed citations
6.
Zou, Jun, Dandan Hu, Harsh Raman, et al.. (2016). Co-linearity and divergence of the A subgenome of Brassica juncea compared with other Brassica species carrying different A subgenomes. BMC Genomics. 17(1). 18–18. 17 indexed citations
7.
Zou, Jun, Harsh Raman, Dandan Hu, et al.. (2014). Constructing a dense genetic linkage map and mapping QTL for the traits of flower development in Brassica carinata. Theoretical and Applied Genetics. 127(7). 1593–1605. 27 indexed citations
8.
Hou, Jinna, Long Yan, Harsh Raman, et al.. (2012). A Tourist-like MITE insertion in the upstream region of the BnFLC.A10 gene is associated with vernalization requirement in rapeseed (Brassica napus L.). BMC Plant Biology. 12(1). 238–238. 84 indexed citations
9.
Zou, Jun, et al.. (2012). A genetic linkage map of Brassica carinata constructed with a doubled haploid population. Theoretical and Applied Genetics. 125(6). 1113–1124. 28 indexed citations
10.
Wang, Xingxing, Chunyu Zhang, Lingjuan Li, et al.. (2012). Unraveling the Genetic Basis of Seed Tocopherol Content and Composition in Rapeseed (Brassica napus L.). PLoS ONE. 7(11). e50038–e50038. 37 indexed citations
11.
Jiang, Congcong, Nirala Ramchiary, Mina Jin, et al.. (2011). Structural and functional comparative mapping between the Brassica A genomes in allotetraploid Brassica napus and diploid Brassica rapa. Theoretical and Applied Genetics. 123(6). 927–941. 25 indexed citations
12.
13.
Xiao, Yong, et al.. (2010). Development of a population for substantial new type Brassica napus diversified at both A/C genomes. Theoretical and Applied Genetics. 121(6). 1141–1150. 37 indexed citations
14.
Yang, Mei, Guangda Ding, Lei Shi, Fangsen Xu, & Jinling Meng. (2010). Detection of QTL for phosphorus efficiency at vegetative stage in Brassica napus. Plant and Soil. 339(1-2). 97–111. 54 indexed citations
15.
Wang, Xingxing, Daguang Cai, Chunyu Zhang, et al.. (2009). Genetic mapping, cloning, and functional characterization of the BnaX.VTE4 gene encoding a γ-tocopherol methyltransferase from oilseed rape. Theoretical and Applied Genetics. 119(3). 567–575. 22 indexed citations
16.
Wang, Nian, Yajie Wang, Fang Tian, et al.. (2008). A functional genomics resource for Brassica napus: development of an EMS mutagenized population and discovery of FAE1 point mutations by TILLING. New Phytologist. 180(4). 751–765. 107 indexed citations
17.
Li, Maoteng, Chunyu Zhang, Wei Qian, & Jinling Meng. (2007). Genetic Diversity of Brassica Species Revealed by Amplified Fragment Length Polymorphism and Simple Sequence Repeat Markers. Horticulture Environment and Biotechnology. 48(1). 9–15. 3 indexed citations
18.
19.
Liu, Liezhao, Jinling Meng, Na Lin, et al.. (2006). QTL Mapping of Seed Coat Color for Yellow Seeded Brassica napus. Acta Genetica Sinica. 33(2). 181–187. 19 indexed citations
20.
Zhao, Jianwei, Joshua A. Udall, Pablo Quijada, et al.. (2005). Quantitative trait loci for resistance to Sclerotinia sclerotiorum and its association with a homeologous non-reciprocal transposition in Brassica napus L.. Theoretical and Applied Genetics. 112(3). 509–516. 125 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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