Michael A. Gore

17.2k total citations · 6 hit papers
139 papers, 10.7k citations indexed

About

Michael A. Gore is a scholar working on Plant Science, Genetics and Molecular Biology. According to data from OpenAlex, Michael A. Gore has authored 139 papers receiving a total of 10.7k indexed citations (citations by other indexed papers that have themselves been cited), including 106 papers in Plant Science, 48 papers in Genetics and 30 papers in Molecular Biology. Recurrent topics in Michael A. Gore's work include Genetic Mapping and Diversity in Plants and Animals (38 papers), Genetics and Plant Breeding (25 papers) and Genetic and phenotypic traits in livestock (20 papers). Michael A. Gore is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (38 papers), Genetics and Plant Breeding (25 papers) and Genetic and phenotypic traits in livestock (20 papers). Michael A. Gore collaborates with scholars based in United States, China and Canada. Michael A. Gore's co-authors include Edward S. Buckler, Jianming Yu, Peter J. Bradbury, Zhiwu Zhang, Alexander E. Lipka, Jason A. Peiffer, Chengsong Zhu, Elhan S. Ersoz, Feng Tian and Meng Li and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Michael A. Gore

138 papers receiving 10.5k citations

Hit Papers

GAPIT: genome association and prediction integrated tool 2008 2026 2014 2020 2012 2010 2008 2009 2012 500 1000 1.5k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. GAPIT: genome association and prediction integrated tool
    2012 1.6k citations Alexander E. Lipka, Michael A. Gore et al. profile →
  2. Mixed linear model approach adapted for genome-wide association studies
    2010 1.5k citations Michael A. Gore, Jianming Yu et al. profile →
  3. Status and Prospects of Association Mapping in Plants
    2008 960 citations Michael A. Gore, Edward S. Buckler et al. The Plant Genome profile →
  4. A First-Generation Haplotype Map of Maize
    2009 536 citations Michael A. Gore, Edward S. Buckler et al. profile →
  5. Field-based phenomics for plant genetics research
    2012 429 citations Michael A. Gore, Douglas J. Hunsaker et al. profile →
  6. Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning
    2017 305 citations Michael A. Gore et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael A. Gore United States 45 8.7k 4.1k 2.0k 951 744 139 10.7k
Jianbing Yan China 65 11.6k 1.3× 7.2k 1.7× 4.0k 2.0× 406 0.4× 1.2k 1.6× 208 14.5k
Lizhong Xiong China 71 17.7k 2.0× 2.7k 0.7× 8.7k 4.4× 1.0k 1.1× 699 0.9× 161 20.3k
Isabel Roldán-Ruíz Belgium 44 3.9k 0.5× 2.1k 0.5× 1.4k 0.7× 827 0.9× 455 0.6× 184 6.6k
Zhiwu Zhang United States 42 12.6k 1.4× 8.6k 2.1× 2.9k 1.5× 406 0.4× 1.4k 1.9× 149 16.9k
Patrick J. Brown United States 35 5.8k 0.7× 3.7k 0.9× 1.4k 0.7× 370 0.4× 1.2k 1.7× 91 7.3k
Jesse Poland United States 59 13.1k 1.5× 8.7k 2.1× 2.4k 1.2× 1.3k 1.4× 1.5k 2.1× 193 16.2k
Zhonghu He China 69 13.4k 1.5× 4.3k 1.1× 1.9k 0.9× 680 0.7× 2.9k 3.9× 438 14.9k
Albrecht E. Melchinger Germany 75 17.6k 2.0× 12.2k 3.0× 3.0k 1.5× 557 0.6× 1.8k 2.4× 399 19.7k
Chengcai Chu China 80 16.7k 1.9× 3.2k 0.8× 7.4k 3.8× 318 0.3× 731 1.0× 248 19.1k
Ray Ming United States 48 7.0k 0.8× 2.6k 0.6× 4.9k 2.5× 382 0.4× 285 0.4× 230 9.9k

Countries citing papers authored by Michael A. Gore

Since Specialization
Citations

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

Fields of papers citing papers by Michael A. Gore

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael A. Gore. 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 Michael A. Gore. The network helps show where Michael A. Gore may publish in the future.

Co-authorship network of co-authors of Michael A. Gore

This figure shows the co-authorship network connecting the top 25 collaborators of Michael A. Gore. A scholar is included among the top collaborators of Michael A. Gore 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 Michael A. Gore. Michael A. Gore 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.
Lin, Meng, Richard Bourgault, Susanne Matschi, et al.. (2024). Integrative multiomic analysis identifies genes associated with cuticular wax biogenesis in adult maize leaves. G3 Genes Genomes Genetics. 2 indexed citations
2.
Robbins, Kelly R., et al.. (2022). Quantitative Genetic Analysis of Interactions in the Pepper– Phytophthora capsici Pathosystem. Molecular Plant-Microbe Interactions. 35(11). 1018–1033. 3 indexed citations
3.
Li, Xiaowei, R. Tanaka, Joshua C. Wood, et al.. (2022). Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain. Genetics. 221(4). 24 indexed citations
4.
Campbell, Malachy T., Haixiao Hu, Melanie Caffe, et al.. (2022). Generalizable approaches for genomic prediction of metabolites in plants. The Plant Genome. 15(2). e20205–e20205. 7 indexed citations
5.
Albert, Elise, Sung Soo Kim, Maria Magallanes‐Lundback, et al.. (2022). Genome-wide association identifies a missing hydrolase for tocopherol synthesis in plants. Proceedings of the National Academy of Sciences. 119(23). 22 indexed citations
6.
Tanaka, R., Xiaowei Li, Laura E. Tibbs‐Cortes, et al.. (2022). Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain. The Plant Genome. 16(4). e20276–e20276. 2 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.
Ferguson, John N., Samuel B. Fernandes, Brandon Monier, et al.. (2021). Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions. PLANT PHYSIOLOGY. 187(3). 1481–1500. 61 indexed citations
9.
Wang, Diane, Martín Venturas, D. S. Mackay, et al.. (2020). Use of hydraulic traits for modeling genotype‐specific acclimation in cotton under drought. New Phytologist. 228(3). 898–909. 8 indexed citations
10.
Matschi, Susanne, Richard Bourgault, Paul Steinbach, et al.. (2020). Structure‐function analysis of the maize bulliform cell cuticle and its potential role in dehydration and leaf rolling. Plant Direct. 4(10). e00282–e00282. 39 indexed citations
11.
Owens, Brenda F., Deepu Mathew, Christine Diepenbrock, et al.. (2019). Genome-Wide Association Study and Pathway-Level Analysis of Kernel Color in Maize. G3 Genes Genomes Genetics. 9(6). 1945–1955. 23 indexed citations
12.
Grover, Corrinne E., Mi‐Jeong Yoo, Meng Lin, et al.. (2019). Genetic Analysis of the Transition from Wild to Domesticated Cotton ( Gossypium hirsutum L.). G3 Genes Genomes Genetics. 10(2). 731–754. 18 indexed citations
13.
Kremling, Karl A., Christine Diepenbrock, Michael A. Gore, Edward S. Buckler, & Nonoy Bandillo. (2019). Transcriptome-Wide Association Supplements Genome-Wide Association in Zea mays. G3 Genes Genomes Genetics. 9(9). 3023–3033. 52 indexed citations
14.
González-Pérez, Lorena, José Crossa, Paulino Pérez‐Rodríguez, et al.. (2019). Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat. G3 Genes Genomes Genetics. 9(4). 1231–1247. 107 indexed citations
15.
Montilla‐Bascón, Gracia, Owen A. Hoekenga, Nicholas A. Tinker, et al.. (2019). Multivariate Genome-Wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat ( Avena sativa L.). G3 Genes Genomes Genetics. 9(9). 2963–2975. 35 indexed citations
16.
Santos, J., Samuel B. Fernandes, Scott McCoy, et al.. (2019). Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum. G3 Genes Genomes Genetics. 10(2). 769–781. 30 indexed citations
17.
Hu, Haixiao, Juan J. Gutiérrez-González, Xinfang Liu, et al.. (2019). Heritable temporal gene expression patterns correlate with metabolomic seed content in developing hexaploid oat seed. Plant Biotechnology Journal. 18(5). 1211–1222. 19 indexed citations
18.
Pauli, Duke, Min Ren, Matthew A. Jenks, et al.. (2018). Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture. G3 Genes Genomes Genetics. 8(4). 1147–1160. 10 indexed citations
19.
Thompson, Alison L., Duke Pauli, Pernell Tomasi, et al.. (2017). Chemical variation for fiber cuticular wax levels in upland cotton (Gossypium hirsutum L.) evaluated under contrasting irrigation regimes. Industrial Crops and Products. 100. 153–162. 13 indexed citations
20.
Gore, Michael A., et al.. (2014). Challenges and Perspectives on Improving Heat and Drought Stress Resilience in Cotton. ˜The œjournal of cotton science/Journal of cotton science. 18(3). 393–409. 48 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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