Yanming Di

823 total citations
25 papers, 552 citations indexed

About

Yanming Di is a scholar working on Molecular Biology, Genetics and Plant Science. According to data from OpenAlex, Yanming Di has authored 25 papers receiving a total of 552 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 9 papers in Genetics and 5 papers in Plant Science. Recurrent topics in Yanming Di's work include Gene expression and cancer classification (9 papers), Molecular Biology Techniques and Applications (7 papers) and Genetic Associations and Epidemiology (6 papers). Yanming Di is often cited by papers focused on Gene expression and cancer classification (9 papers), Molecular Biology Techniques and Applications (7 papers) and Genetic Associations and Epidemiology (6 papers). Yanming Di collaborates with scholars based in United States and Canada. Yanming Di's co-authors include Jason S. Cumbie, Jeff H. Chang, Daniel W. Schafer, Joseph W. Spatafora, Justin Elser, Pankaj Jaiswal, Gu Mi, Kerry L. McPhail, Brian J. Knaus and Kathryn E. Bushley and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Journal of Experimental Botany.

In The Last Decade

Yanming Di

25 papers receiving 539 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yanming Di United States 11 342 192 79 78 52 25 552
Zhao Xu China 13 392 1.1× 233 1.2× 40 0.5× 30 0.4× 15 0.3× 21 586
Junjie Shao China 15 380 1.1× 83 0.4× 220 2.8× 53 0.7× 55 1.1× 35 663
Yajun Liu China 10 133 0.4× 189 1.0× 39 0.5× 29 0.4× 100 1.9× 27 381
Giuseppe Profiti Italy 7 299 0.9× 157 0.8× 45 0.6× 23 0.3× 19 0.4× 12 456
Changhoon Kim South Korea 9 195 0.6× 232 1.2× 93 1.2× 49 0.6× 11 0.2× 20 491
Dianna G. Fisk United States 6 1.0k 3.0× 190 1.0× 87 1.1× 32 0.4× 16 0.3× 8 1.1k
Lihua Jin Japan 13 427 1.2× 241 1.3× 26 0.3× 13 0.2× 39 0.8× 25 631
Olivier Langella France 13 537 1.6× 287 1.5× 68 0.9× 26 0.3× 12 0.2× 21 761
Eugenio Mancera United States 13 755 2.2× 249 1.3× 268 3.4× 29 0.4× 34 0.7× 21 948

Countries citing papers authored by Yanming Di

Since Specialization
Citations

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

Fields of papers citing papers by Yanming Di

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yanming Di

This figure shows the co-authorship network connecting the top 25 collaborators of Yanming Di. A scholar is included among the top collaborators of Yanming Di 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 Yanming Di. Yanming Di 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.
Di, Yanming, et al.. (2023). Detection of Temporal Shifts in Semantics Using Local Graph Clustering. SHILAP Revista de lepidopterología. 5(1). 128–143. 1 indexed citations
2.
Miller, Andrew, et al.. (2022). Characterizing the extracellular matrix transcriptome of cervical, endometrial, and uterine cancers. SHILAP Revista de lepidopterología. 15. 100117–100117. 7 indexed citations
3.
Jiang, Duo, et al.. (2020). Test-statistic correlation and data-row correlation. Statistics & Probability Letters. 167. 108903–108903. 4 indexed citations
4.
Chang, Jeff H., et al.. (2016). Identifying stably expressed genes from multiple RNA-Seq data sets. PeerJ. 4. e2791–e2791. 9 indexed citations
5.
Mi, Gu & Yanming Di. (2015). The Level of Residual Dispersion Variation and the Power of Differential Expression Tests for RNA-Seq Data. PLoS ONE. 10(4). e0120117–e0120117. 1 indexed citations
6.
Mi, Gu, Yanming Di, & Daniel W. Schafer. (2015). Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data. PLoS ONE. 10(3). e0119254–e0119254. 15 indexed citations
7.
Di, Yanming. (2015). Single-gene negative binomial regression models for RNA-Seq data with higher-order asymptotic inference. Statistics and Its Interface. 8(4). 405–418. 5 indexed citations
8.
Jiang, Yuan, et al.. (2014). Family-based association test using normal approximation to gene dropping null distribution. BMC Proceedings. 8(S1). S18–S18. 1 indexed citations
9.
Gouthu, Satyanarayana, et al.. (2014). A comparative study of ripening among berries of the grape cluster reveals an altered transcriptional programme and enhanced ripening rate in delayed berries. Journal of Experimental Botany. 65(20). 5889–5902. 46 indexed citations
10.
Vining, Kelly, Kyle Pomraning, Larry Wilhelm, et al.. (2013). Methylome reorganization during in vitro dedifferentiation and regeneration of Populus trichocarpa. BMC Plant Biology. 13(1). 92–92. 47 indexed citations
11.
Di, Yanming, Sarah C. Emerson, Daniel W. Schafer, Jeffrey A. Kimbrel, & Jeff H. Chang. (2013). Higher order asymptotics for negative binomial regression inferences from RNA-sequencing data. Statistical Applications in Genetics and Molecular Biology. 12(1). 49–70. 9 indexed citations
12.
Bushley, Kathryn E., Pankaj Jaiswal, Jason S. Cumbie, et al.. (2013). The Genome of Tolypocladium inflatum: Evolution, Organization, and Expression of the Cyclosporin Biosynthetic Gene Cluster. PLoS Genetics. 9(6). e1003496–e1003496. 129 indexed citations
13.
Mi, Gu, et al.. (2012). Length Bias Correction in Gene Ontology Enrichment Analysis Using Logistic Regression. PLoS ONE. 7(10). e46128–e46128. 23 indexed citations
14.
Di, Yanming, Daniel W. Schafer, Jason S. Cumbie, & Jeff H. Chang. (2011). The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq. Statistical Applications in Genetics and Molecular Biology. 10(1). 132 indexed citations
15.
Thomas, Alun, Haley Abel, Yanming Di, et al.. (2011). Effect of linkage disequilibrium on the identification of functional variants. Genetic Epidemiology. 35(S1). S115–9. 5 indexed citations
16.
Di, Yanming, et al.. (2011). Power of association tests in the presence of multiple causal variants. BMC Proceedings. 5(S9). S63–S63. 4 indexed citations
17.
Di, Yanming & E. A. Thompson. (2009). Conditional Tests for Localizing Trait Genes. Human Heredity. 68(2). 139–150. 2 indexed citations
18.
Marchani, Elizabeth E., Yanming Di, Yoonha Choi, et al.. (2009). Contrasting identity-by-descent estimators, association studies, and linkage analyses using the Framingham Heart Study data. BMC Proceedings. 3(S7). S102–S102. 5 indexed citations
19.
Basu, Saonli, Yanming Di, & E. A. Thompson. (2008). Exact Trait‐Model‐Free Tests for Linkage Detection in Pedigrees. Annals of Human Genetics. 72(5). 676–682. 5 indexed citations
20.
Sung, Yun Ju, Yanming Di, Audrey Qiuyan Fu, et al.. (2007). Comparison of multipoint linkage analyses for quantitative traits in the CEPH data: parametric LOD scores, variance components LOD scores, and Bayes factors. BMC Proceedings. 1(S1). S93–S93. 14 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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