The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Genomic selection: genome-wide prediction in plant improvement
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Citations
Cites
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001) · Genetics · doi:10.1093/genetics/157.4.1819 · Cited by 7,917
Efficient Methods to Compute Genomic Predictions
VanRaden, P. M. (2008) · Journal of Dairy Science · doi:10.3168/jds.2007-0980 · Cited by 6,050
Genome-wide genetic marker discovery and genotyping using next-generation sequencing
Davey, J. W., Hohenlohe, P. A., Etter, P. D., Boone, J. Q., Catchen, J. M., & Blaxter, M. L. (2011) · Nature Reviews Genetics · doi:10.1038/nrg3012 · Cited by 2,481
Marker-assisted selection: an approach for precision plant breeding in the twenty-first century
Collard, B. C. Y., & Mackill, D. J. (2008) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2007.2170 · Cited by 2,257
Genomic Selection for Crop Improvement
Heffner, E. L., Sorrells, M. E., & Jannink, J. (2009) · Crop Science · doi:10.2135/cropsci2008.08.0512 · Cited by 1,652
Extension of the bayesian alphabet for genomic selection
Habier, D., Fernando, R. L., Kizilkaya, K., & Garrick, D. J. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-186 · Cited by 1,224
Genomic selection in plant breeding: from theory to practice
Jannink, J.-L., Lorenz, A. J., & Iwata, H. (2010) · Briefings in Functional Genomics · doi:10.1093/bfgp/elq001 · Cited by 1,206
Genomic selection: prediction of accuracy and maximisation of long term response
Goddard, M. (2009) · Genetica · doi:10.1007/s10709-008-9308-0 · Cited by 1,089
Cited by
Accelerating crop genetic gains with genomic selection
Voss-Fels, K. P., Cooper, M., & Hayes, B. J. (2019) · Theoretical and Applied Genetics · doi:10.1007/s00122-018-3270-8 · Cited by 381
A deep convolutional neural network approach for predicting phenotypes from genotypes
Ma, W., Qiu, Z., Song, J., Li, J., Cheng, Q., Zhai, J., & Ma, C. (2018) · Planta · doi:10.1007/s00425-018-2976-9 · Cited by 341
Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants
Xu, Y., Liu, X., Fu, J., Wang, H., Wang, J., Huang, C., Prasanna, B. M., Olsen, M. S., Wang, G., & Zhang, A. (2020) · Plant Communications · doi:10.1016/j.xplc.2019.100005 · Cited by 281
Gaffney, J., Schussler, J., Löffler, C., Cai, W., Paszkiewicz, S., Messina, C., Groeteke, J., Keaschall, J., & Cooper, M. (2015) · Crop Science · doi:10.2135/cropsci2014.09.0654 · Cited by 193
Genetic Variation and Unintended Risk in the Context of Old and New Breeding Techniques
Singer, S. D., Laurie, J. D., Bilichak, A., Kumar, S., & Singh, J. (2021) · Critical Reviews in Plant Sciences · doi:10.1080/07352689.2021.1883826 · Cited by 77
Metadata
- Authors
- Desta, Z. A., & Ortiz, R.
- Venue
- Trends in Plant Science
- Publisher
- Elsevier BV
- Published
- 2014-09
- Volume
- 19
- Issue
- 9
- Pages
- 592-601
- ISSN
- 1360-1385 (ISSN-L)
- OpenAlex
- W1998447523
- Cited by
- 688
- References
- 82
- FWCI
- 4.77
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Genomic selection: genome-wide prediction in plant improvement. https://app.cochat.ai/papers/10.1016/j.tplants.2014.05.006
- The paper
- Desta, Z. A., & Ortiz, R. (2014). Genomic selection: genome-wide prediction in plant improvement. Trends in Plant Science. https://doi.org/10.1016/j.tplants.2014.05.006
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
