Joanna Szyda

1.7k total citations
86 papers, 1.2k citations indexed

About

Joanna Szyda is a scholar working on Genetics, Plant Science and Agronomy and Crop Science. According to data from OpenAlex, Joanna Szyda has authored 86 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 68 papers in Genetics, 24 papers in Plant Science and 22 papers in Agronomy and Crop Science. Recurrent topics in Joanna Szyda's work include Genetic and phenotypic traits in livestock (57 papers), Genetic Mapping and Diversity in Plants and Animals (42 papers) and Genetics and Plant Breeding (12 papers). Joanna Szyda is often cited by papers focused on Genetic and phenotypic traits in livestock (57 papers), Genetic Mapping and Diversity in Plants and Animals (42 papers) and Genetics and Plant Breeding (12 papers). Joanna Szyda collaborates with scholars based in Poland, Germany and United States. Joanna Szyda's co-authors include Sigbjørn Lien, Tomasz Suchocki, Jolanta Komisarek, Eli Grindflek, Zengting Liu, T. Strabel, Stanisław Kamiński, E. Ptak, J. Jamrozik and Norman Arnheim and has published in prestigious journals such as Gastroenterology, PLoS ONE and Scientific Reports.

In The Last Decade

Joanna Szyda

84 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Joanna Szyda Poland 19 956 304 283 268 203 86 1.2k
J. Casellas Spain 24 1.2k 1.3× 399 1.3× 251 0.9× 189 0.7× 486 2.4× 136 1.7k
E. Lipkin Israel 18 693 0.7× 226 0.7× 258 0.9× 213 0.8× 115 0.6× 42 1.0k
Christine A. Ford New Zealand 7 1.2k 1.3× 241 0.8× 157 0.6× 329 1.2× 145 0.7× 8 1.4k
R. M. Thallman United States 25 1.5k 1.5× 583 1.9× 202 0.7× 232 0.9× 642 3.2× 79 1.9k
A. Nejati‐Javaremi Iran 17 1.0k 1.1× 400 1.3× 134 0.5× 217 0.8× 291 1.4× 62 1.3k
L. Andersson‐Eklund Sweden 13 1.0k 1.1× 177 0.6× 167 0.6× 322 1.2× 227 1.1× 23 1.2k
Nina Schulman Finland 13 816 0.9× 315 1.0× 103 0.4× 183 0.7× 96 0.5× 18 930
Paulette Berzi Belgium 9 1.7k 1.8× 265 0.9× 221 0.8× 568 2.1× 158 0.8× 10 1.9k
Gregório Miguel Ferreira de Camargo Brazil 19 1.0k 1.1× 436 1.4× 129 0.5× 124 0.5× 252 1.2× 91 1.2k
Yoshinobu Uemoto Japan 22 977 1.0× 163 0.5× 196 0.7× 122 0.5× 521 2.6× 95 1.3k

Countries citing papers authored by Joanna Szyda

Since Specialization
Citations

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

Fields of papers citing papers by Joanna Szyda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Joanna Szyda

This figure shows the co-authorship network connecting the top 25 collaborators of Joanna Szyda. A scholar is included among the top collaborators of Joanna Szyda 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 Joanna Szyda. Joanna Szyda 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.
Szyda, Joanna, et al.. (2024). Nextflow vs. plain bash: different approaches to the parallelization of SNP calling from the whole genome sequence data. NAR Genomics and Bioinformatics. 6(2). lqae040–lqae040. 1 indexed citations
2.
Biecek, Przemysław, et al.. (2024). An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data—Circumventing the p >> n Problem. International Journal of Molecular Sciences. 25(9). 4715–4715. 3 indexed citations
3.
Borowczyk, Martyna, Joanna Szyda, Katarzyna Ziemnicka, et al.. (2024). Genetic predisposition to differentiated thyroid cancer among Polish population. Polskie Archiwum Medycyny Wewnętrznej. 134(3). 1 indexed citations
4.
Mroczek, Magdalena, et al.. (2023). Genetics, Genomics and Emerging Molecular Therapies of Pancreatic Cancer. Cancers. 15(3). 779–779. 9 indexed citations
5.
Zielak-Steciwko, Anna E., et al.. (2022). An effect of large-scale deletions and duplications on transcript expression. Functional & Integrative Genomics. 23(1).
6.
Szyda, Joanna, et al.. (2022). Genome-Wide Genomic and Functional Association Study for Workability and Calving Traits in Holstein Cattle. Animals. 12(9). 1127–1127. 3 indexed citations
8.
Suchocki, Tomasz, et al.. (2020). The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines. Journal of Applied Genetics. 61(4). 607–616. 5 indexed citations
9.
Szyda, Joanna, et al.. (2015). Review of alignment and SNP calling algorithms for next-generation sequencing data. Journal of Applied Genetics. 57(1). 71–79. 43 indexed citations
10.
Suchocki, Tomasz, et al.. (2014). Do rare variants contribute to the genomic prediction accuracy. Proceedings of the World Congress on Genetics Applied to Livestock Production. 488. 1 indexed citations
11.
Oleński, Kamil, Anna Cieślińska, Tomasz Suchocki, Joanna Szyda, & Stanisław Kamiński. (2012). Polymorphism in coding and regulatory sequences of beta-casein gene is associated with milk production traits in Holstein-Friesian cattle. Animal Science Papers and Reports. 30(1). 5–12. 18 indexed citations
12.
Cromie, A.R., B. Wickham, John F. Kearney, et al.. (2010). International Genomic Co-operation; Who, what, when, where, why and how?. Open Repository and Bibliography (University of Liège). 42(42). 72. 8 indexed citations
13.
Szyda, Joanna, et al.. (2009). Incorporation of correlation between SNPs into the genomic evaluation model. Bulletin - International Bull Evaluation Service/Interbull bulletin. 193. 3 indexed citations
14.
Szyda, Joanna, et al.. (2009). The Polish genomic breeding value estimation project. Bulletin - International Bull Evaluation Service/Interbull bulletin. 43. 4 indexed citations
15.
Komisarek, Jolanta, et al.. (2005). Impact of leptin gene polymorphisms on breedingvalue for milk production traits in cattle. Journal of Animal and Feed Sciences. 14(3). 491–500. 25 indexed citations
16.
Strabel, T., E. Ptak, Joanna Szyda, & J. Jamrozik. (2004). Estimates of genetic parameters for protein yield of Polish Black-and-White cattle with multiple-lactation rendom regression test-day model. 22(2). 1 indexed citations
17.
Strabel, T., E. Ptak, Joanna Szyda, & J. Jamrozik. (2004). Multiple-lactation random regression test-day model for Polish Black and White cattle. Bulletin - International Bull Evaluation Service/Interbull bulletin. 133. 12 indexed citations
18.
Strabel, T., Joanna Szyda, E. Ptak, & J. Jamrozik. (2003). Comparison of random regression test-day models for production traits of dairy cattle in Poland. Bulletin - International Bull Evaluation Service/Interbull bulletin. 197. 6 indexed citations
19.
Szyda, Joanna, Henner Simianer, & Sigbjørn Lien. (2000). Sex ratio distortion in bovine sperm correlates to recombination in the pseudoautosomal region. Genetics Research. 75(1). 53–59. 16 indexed citations
20.
Szyda, Joanna, et al.. (1999). Modelling test day data from dairy cattle. Journal of Applied Genetics. 40(2). 103–116. 3 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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