The DOI matches its Crossref record; 100 of the 100 references it cites were found in a registry or an index.
Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence
Open accessAbstract
Breeding crops for high yield and superior adaptability to new and variable climates is imperative to ensure continued food security, biomass production, and ecosystem services. Advances in genomics and phenomics are delivering insights into the complex biological mechanisms that underlie plant functions in response to environmental perturbations. However, linking genotype to phenotype remains a huge challenge and is hampering the optimal application of high-throughput genomics and phenomics to advanced breeding. Critical to success is the need to assimilate large amounts of data into biologically meaningful interpretations. Here, we present the current state of genomics and field phenomics, explore emerging approaches and challenges for multiomics big data integration by means of next-generation (Next-Gen) artificial intelligence (AI), and propose a workable path to improvement.
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Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
Cited by
Xu, Y., Zhang, X., Li, H., Zheng, H., Zhang, J., Olsen, M. S., Varshney, R. K., Prasanna, B. M., & Qian, Q. (2022) · Molecular Plant · doi:10.1016/j.molp.2022.09.001 · Cited by 363
Deploying artificial intelligence for climate change adaptation
Leal Filho, W., Wall, T., Rui Mucova, S. A., Nagy, G. J., Balogun, A.-L., Luetz, J. M., Ng, A. W., Kovaleva, M., Safiul Azam, F. M., Alves, F., et al. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121662 · Cited by 266
Crossa, J., Martini, J. W. R., Gianola, D., Pérez-Rodríguez, P., Jarquin, D., Juliana, P., Montesinos-López, O., & Cuevas, J. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01168 · Cited by 79
Nakhle, F., & Harfouche, A. L. (2021) · Patterns · doi:10.1016/j.patter.2021.100323 · Cited by 28
“Although next-generation wireless network technologies such as 5G aspire to provide higher bandwidth and lower latency, 25,123 further concerns arise from the compute-centric approach, including (1) energy efficiency where data transmission is energy intensive, 124 (2) potential privacy violations…”
BackgroundMethodInfluentialSelf-citationMelsen, K. A. C., Kunst, J. F., Crossa, J., Krause, M. R., van Eeuwijk, F. A., Kruijer, W., & Peeters, C. F. W. (2025) · Biometrical Journal · doi:10.1002/bimj.70081 · Cited by 2
Metadata
- Authors
- Harfouche, A. L., Jacobson, D. A., Kainer, D., Romero, J. C., Harfouche, A. H., Scarascia Mugnozza, G., Moshelion, M., Tuskan, G. A., Keurentjes, J. J. B., & Altman, A.
- Venue
- Trends in Biotechnology
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 37
- Issue
- 11
- Pages
- 1217-1235
- ISSN
- 0167-7799 (ISSN-L)
- OpenAlex
- W2949474492
- Cited by
- 292
- References
- 100
- FWCI
- 4.19
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Harfouche, A. L., Jacobson, D. A., Kainer, D., Romero, J. C., Harfouche, A. H., Scarascia Mugnozza, G., Moshelion, M., Tuskan, G. A., Keurentjes, J. J. B., & Altman, A. (2019). Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence. Trends in Biotechnology. https://doi.org/10.1016/j.tibtech.2019.05.007
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