Ina Hoeschele

4.3k total citations
84 papers, 2.9k citations indexed

About

Ina Hoeschele is a scholar working on Genetics, Plant Science and Molecular Biology. According to data from OpenAlex, Ina Hoeschele has authored 84 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 65 papers in Genetics, 28 papers in Plant Science and 27 papers in Molecular Biology. Recurrent topics in Ina Hoeschele's work include Genetic Mapping and Diversity in Plants and Animals (51 papers), Genetic and phenotypic traits in livestock (49 papers) and Genetics and Plant Breeding (21 papers). Ina Hoeschele is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (51 papers), Genetic and phenotypic traits in livestock (49 papers) and Genetics and Plant Breeding (21 papers). Ina Hoeschele collaborates with scholars based in United States, Germany and Australia. Ina Hoeschele's co-authors include Nan Bing, Alberto de la Fuente, P.M. VanRaden, Pedro Mendes, Pekka Uimari, Georg Thaller, Bruce Tier, I.J.M. de Boer, Bing Liu and T.R. Meinert and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Ina Hoeschele

83 papers receiving 2.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ina Hoeschele United States 30 1.8k 991 888 236 225 84 2.9k
Ana I. Vázquez United States 25 1.3k 0.7× 511 0.5× 392 0.4× 198 0.8× 282 1.3× 75 2.0k
Dénis Laloë France 28 2.0k 1.1× 585 0.6× 1.1k 1.2× 394 1.7× 526 2.3× 97 3.5k
Florence Jaffrézic France 22 991 0.5× 329 0.3× 1.2k 1.4× 247 1.0× 340 1.5× 78 2.7k
Simon Boitard France 18 1.7k 0.9× 223 0.2× 724 0.8× 256 1.1× 186 0.8× 32 2.7k
Miguel Pérez‐Enciso Spain 40 3.5k 2.0× 1.1k 1.1× 1.3k 1.4× 1.2k 4.9× 340 1.5× 140 5.0k
Dahlia M. Nielsen United States 28 4.3k 2.4× 3.6k 3.7× 1.5k 1.6× 165 0.7× 384 1.7× 51 6.7k
Seoae Cho South Korea 24 892 0.5× 384 0.4× 723 0.8× 261 1.1× 199 0.9× 78 1.9k
J.A.M. Leunissen Netherlands 17 607 0.3× 756 0.8× 1.8k 2.0× 75 0.3× 67 0.3× 29 3.5k
Eva K.F. Chan United States 27 799 0.4× 589 0.6× 975 1.1× 87 0.4× 112 0.5× 57 2.5k
Jean‐Louis Foulley France 18 1.2k 0.7× 207 0.2× 224 0.3× 268 1.1× 277 1.2× 55 1.7k

Countries citing papers authored by Ina Hoeschele

Since Specialization
Citations

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

Fields of papers citing papers by Ina Hoeschele

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ina Hoeschele

This figure shows the co-authorship network connecting the top 25 collaborators of Ina Hoeschele. A scholar is included among the top collaborators of Ina Hoeschele 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 Ina Hoeschele. Ina Hoeschele 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.
Zimmerman, Kathy, David L. Panciera, Ina Hoeschele, et al.. (2018). Adrenocortical Challenge Response and Genomic Analyses in Scottish Terriers With Increased Alkaline Phosphate Activity. Frontiers in Veterinary Science. 5. 231–231. 6 indexed citations
2.
Connolly, Nina P., Amol C. Shetty, Jesse A. Stokum, et al.. (2018). Cross-species transcriptional analysis reveals conserved and host-specific neoplastic processes in mammalian glioma. Scientific Reports. 8(1). 1180–1180. 18 indexed citations
3.
Kakko, Sakari, Minna Tamminen, Peter von Rohr, et al.. (2009). Genome scan for loci regulating HDL cholesterol levels in Finnish extended pedigrees with early coronary heart disease. European Journal of Human Genetics. 18(5). 604–613. 4 indexed citations
4.
Zhou, Lecong, Santiago X. Mideros, Lei Bao, et al.. (2009). Infection and genotype remodel the entire soybean transcriptome. BMC Genomics. 10(1). 49–49. 46 indexed citations
5.
Bing, Nan & Ina Hoeschele. (2005). Genetical Genomics Analysis of a Yeast Segregant Population for Transcription Network Inference. Genetics. 170(2). 533–542. 74 indexed citations
6.
Pfister‐Genskow, Martha, Thomas E. Patterson, J. Betthauser, et al.. (2004). Identification of Differentially Expressed Genes in Individual Bovine Preimplantation Embryos Produced by Nuclear Transfer: Improper Reprogramming of Genes Required for Development1. Biology of Reproduction. 72(3). 546–555. 84 indexed citations
7.
8.
Hoeschele, Ina, et al.. (2000). A Note on Algorithms for Genotype and Allele Elimination in Complex Pedigrees With Incomplete Genotype Data. Genetics. 156(4). 2051–2062. 6 indexed citations
9.
Zhang, Qin, Didier Boichard, Ina Hoeschele, et al.. (1998). Mapping Quantitative Trait Loci for Milk Production and Health of Dairy Cattle in a Large Outbred Pedigree. Genetics. 149(4). 1959–1973. 186 indexed citations
10.
Uimari, Pekka, et al.. (1996). Analysis of QTL workshop I granddaughter design data using least-squares, residual maximum likelihood and bayesian methods.. 2. 1–20. 14 indexed citations
11.
Uimari, Pekka, Georg Thaller, & Ina Hoeschele. (1996). The Use of Multiple Markers in a Bayesian Method for Mapping Quantitative Trait Loci. Genetics. 143(4). 1831–1842. 61 indexed citations
12.
Georges, Michel, Dahlia M. Nielsen, Margaret J. Mackinnon, et al.. (1995). Mapping quantitative trait loci controlling milk production by exploiting progeny testing. Open Repository and Bibliography (University of Liège). 29 indexed citations
13.
Hoeschele, Ina & Bruce Tier. (1995). Estimation of variance components of threshold characters by marginal posterior modes and means via Gibbs sampling. Genetics Selection Evolution. 27(6). 84 indexed citations
14.
Boer, I.J.M. de & Ina Hoeschele. (1993). Genetic evaluation methods for populations with dominance and inbreeding. Theoretical and Applied Genetics. 86-86(2-3). 245–258. 78 indexed citations
15.
Hoeschele, Ina & P.M. VanRaden. (1993). Bayesian analysis of linkage between genetic markers and quantitative trait loci. I. Prior knowledge. Theoretical and Applied Genetics. 85(8). 953–960. 46 indexed citations
16.
Hoeschele, Ina & P.M. VanRaden. (1993). Bayesian analysis of linkage between genetic markers and quantitative trait loci. II. Combining prior knowledge with experimental evidence. Theoretical and Applied Genetics. 85(8). 946–952. 40 indexed citations
17.
Hoeschele, Ina & Eduardo Romano. (1993). On the use of marker information from granddaughter designs. Journal of Animal Breeding and Genetics. 110(1-6). 429–449. 4 indexed citations
18.
VanRaden, P.M., T.J. Lawlor, T.H. Short, & Ina Hoeschele. (1992). Use of Reproductive Technology to Estimate Variances and Predict Effects of Gene Interactions. Journal of Dairy Science. 75(10). 2892–2901. 33 indexed citations
19.
Hoeschele, Ina. (1989). A note on local maxima in maximum likelihood, restricted maximum likelihood, and baysian estimation of variance components. Journal of Statistical Computation and Simulation. 33(3). 149–160. 10 indexed citations
20.
Hoeschele, Ina. (1988). Genetic evaluation with data presenting evidence of mixed major gene and polygenic inheritance. Theoretical and Applied Genetics. 76(1). 81–92. 37 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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