Timothy Beissinger

1.9k total citations
31 papers, 1.1k citations indexed

About

Timothy Beissinger is a scholar working on Genetics, Plant Science and Molecular Biology. According to data from OpenAlex, Timothy Beissinger has authored 31 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Genetics, 22 papers in Plant Science and 7 papers in Molecular Biology. Recurrent topics in Timothy Beissinger's work include Genetic Mapping and Diversity in Plants and Animals (25 papers), Genetic and phenotypic traits in livestock (15 papers) and Genetics and Plant Breeding (15 papers). Timothy Beissinger is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (25 papers), Genetic and phenotypic traits in livestock (15 papers) and Genetics and Plant Breeding (15 papers). Timothy Beissinger collaborates with scholars based in United States, Germany and Netherlands. Timothy Beissinger's co-authors include Natalia de León, Avjinder S. Kaler, Jason D. Gillman, Larry C. Purcell, Shawn M. Kaeppler, Jeffrey Ross‐Ibarra, Li Wang, Matthew B. Hufford, Candice N. Hirsch and C. Robin Buell and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Plant Cell and Genetics.

In The Last Decade

Timothy Beissinger

31 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Timothy Beissinger United States 16 747 650 260 75 37 31 1.1k
Chouxian Ma China 6 760 1.0× 539 0.8× 304 1.2× 48 0.6× 54 1.5× 7 1.1k
Ümit Seren Austria 10 943 1.3× 716 1.1× 448 1.7× 63 0.8× 29 0.8× 12 1.4k
Ahong Wang China 11 1.2k 1.6× 870 1.3× 355 1.4× 100 1.3× 32 0.9× 14 1.4k
Qijun Weng China 5 945 1.3× 627 1.0× 372 1.4× 44 0.6× 19 0.5× 5 1.2k
Akio Onogi Japan 15 562 0.8× 530 0.8× 117 0.5× 77 1.0× 15 0.4× 41 805
Chuanbei Jiang China 5 439 0.6× 343 0.5× 186 0.7× 26 0.3× 32 0.9× 5 664
Jaime Cuevas Mexico 15 1.5k 2.0× 1.3k 2.0× 127 0.5× 119 1.6× 71 1.9× 28 1.8k
Meng Huang United States 8 1.4k 1.8× 878 1.4× 234 0.9× 188 2.5× 28 0.8× 19 1.7k
Jennifer Jaqueth United States 12 628 0.8× 419 0.6× 233 0.9× 45 0.6× 26 0.7× 16 829

Countries citing papers authored by Timothy Beissinger

Since Specialization
Citations

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

Fields of papers citing papers by Timothy Beissinger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Timothy Beissinger

This figure shows the co-authorship network connecting the top 25 collaborators of Timothy Beissinger. A scholar is included among the top collaborators of Timothy Beissinger 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 Timothy Beissinger. Timothy Beissinger 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.
Zumbach, B., et al.. (2025). Individual plant genetics reveal the control of local adaptation in European maize landraces. BMC Biology. 23(1). 138–138. 1 indexed citations
2.
Wallace, Jason G., James C. Schnable, Judith M. Kolkman, et al.. (2023). Yield prediction through integration of genetic, environment, and management data through deep learning. G3 Genes Genomes Genetics. 13(4). 26 indexed citations
3.
Simianer, Henner, et al.. (2022). learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data. G3 Genes Genomes Genetics. 12(11). 9 indexed citations
4.
Pook, Torsten, et al.. (2022). Imputation of low‐density marker chip data in plant breeding: Evaluation of methods based on sugar beet. The Plant Genome. 15(4). 4 indexed citations
5.
Kaler, Avjinder S., Larry C. Purcell, Timothy Beissinger, & Jason D. Gillman. (2022). Genomic prediction models for traits differing in heritability for soybean, rice, and maize. BMC Plant Biology. 22(1). 87–87. 41 indexed citations
6.
Kumar, Rohit, Christopher Saski, Daniel J. Robertson, et al.. (2021). Genetic Architecture of Maize Rind Strength Revealed by the Analysis of Divergently Selected Populations. Plant and Cell Physiology. 62(7). 1199–1214. 13 indexed citations
7.
Mabry, Makenzie E., Alex C. McAlvay, Hong An, et al.. (2021). The Evolutionary History of Wild, Domesticated, and Feral Brassica oleracea (Brassicaceae). Molecular Biology and Evolution. 38(10). 4419–4434. 58 indexed citations
8.
Thorwarth, Patrick, et al.. (2021). Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks. Frontiers in Plant Science. 12. 699589–699589. 32 indexed citations
9.
10.
Mabry, Makenzie E., et al.. (2020). Evolutionary insights into plant breeding. Current Opinion in Plant Biology. 54. 93–100. 29 indexed citations
11.
Kaler, Avjinder S., Jason D. Gillman, Timothy Beissinger, & Larry C. Purcell. (2020). Comparing Different Statistical Models and Multiple Testing Corrections for Association Mapping in Soybean and Maize. Frontiers in Plant Science. 10. 1794–1794. 154 indexed citations
12.
Sekhon, Rajandeep S., Christopher Saski, Rohit Kumar, et al.. (2019). Integrated Genome-Scale Analysis Identifies Novel Genes and Networks Underlying Senescence in Maize. The Plant Cell. 31(9). 1968–1989. 67 indexed citations
13.
Guill, Katherine E., et al.. (2019). Single-plant GWAS coupled with bulk segregant analysis allows rapid identification and corroboration of plant-height candidate SNPs. BMC Plant Biology. 19(1). 412–412. 27 indexed citations
14.
Wang, Li, et al.. (2017). The interplay of demography and selection during maize domestication and expansion. Genome biology. 18(1). 215–215. 133 indexed citations
15.
Beissinger, Timothy & Gota Morota. (2017). Medical Subject Heading (MeSH) annotations illuminate maize genetics and evolution. Plant Methods. 13(1). 8–8. 3 indexed citations
16.
Morota, Gota, Timothy Beissinger, & Francisco Peñagaricano. (2016). MeSH-Informed Enrichment Analysis and MeSH-Guided Semantic Similarity Among Functional Terms and Gene Products in Chicken. G3 Genes Genomes Genetics. 6(8). 2447–2453. 8 indexed citations
17.
Beissinger, Timothy, et al.. (2015). Large effect QTL explain natural phenotypic variation for the developmental timing of vegetative phase change in maize (Zea mays L.). Theoretical and Applied Genetics. 128(3). 529–538. 17 indexed citations
18.
Beissinger, Timothy, Mahmood Gholami, Malena Erbe, et al.. (2015). Using the variability of linkage disequilibrium between subpopulations to infer sweeps and epistatic selection in a diverse panel of chickens. Heredity. 116(2). 158–166. 10 indexed citations
19.
Lorenz, Aaron J., et al.. (2015). Selection for Silage Yield and Composition Did Not Affect Genomic Diversity Within the Wisconsin Quality Synthetic Maize Population. G3 Genes Genomes Genetics. 5(4). 541–549. 5 indexed citations
20.
Wu, Xiao‐Lin, Timothy Beissinger, Stewart Bauck, et al.. (2011). A Primer on High-Throughput Computing for Genomic Selection. SHILAP Revista de lepidopterología. 2. 4–4. 12 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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