Non-Peer Reviewed Articles (2016 ~ 2024)

Notes/Symbols Legend

* Authors contributed equally to the article

2024 (2 articles)Top ⇪

Shrestha N*, A Powadi*, J Davis, TT Ayanlade, H Liu, MC Tross, RK Mathivanan, J Bares, L Lopez-Corona, J Turkus, L Coffey, T Jubery, Y Ge, S Sarkar, JC Schnable, B Ganapathysubramanian, PS Schnable (2024) Plot-level satellite imagery can substitute for UAVs in assessing maize phenotypes across multistate field trials. agriRxiv, 2024.00251. doi:10.31220/agriRxiv.2024.00251

[ Abstract | 13 May 2024 ]
Ganapathysubramanian B, JMP Bell, G Kantor, N Merchant, S Sarkar, PS Schnable, M Segovia, A Singh, AK Singh (2024) AIIRA: AI institute for resilient agriculture. AI Magazine, 45(1): 94-98. doi:10.1002/aaai.12151

[ Abstract | Full Text PDF | 9 February 2024 ]

2023 (1 article)Top ⇪

Li D, Q Wang, Y Tian, X Lyv, H Zhang, Y Sun, H Hong, H Gao, YF Li, C Zhao, J Wang, R Wang, J Yang, B Liu, PS Schnable, JC Schnable, YH Li, LJ Qiu (2023) Transcriptome brings variations of gene expression, alternative splicing, and structural variations into gene-scale trait dissection in soybean. bioRχiv, 2023.07.03.545230. doi:10.1101/2023.07.03.545230

[ Abstract | Full Text PDF | Supplemental Materials | 4 July 2023 ]

2022 (1 article)Top ⇪

Clarke J, JCM Dekkers, D Ertl, CJ Lawrence-Dill, E Lyons, BM Murdoch, NM Scott, CK Tuggle, PS Schnable (2022) Community perspectives: Genome to phenome in agricultural sciences. OSF Preprints, 12 Dec. 2022. doi:10.31219/osf.io/p89vk

[ Abstract | 12 December 2022 ]

2021 (1 article)Top ⇪

Zhou Y, M Kamruzzaman, PS Schnable, B Krishnamoorthy, A Kalyanaraman, B Wang (2021) Pheno-Mapper: an interactive toolbox for the visual exploration of phenomics data. arXiv, 2106.13397.

[ Abstract | 25 June 2021 ]

2020 (2 articles)Top ⇪

Kusmec A, CT Yeh, The Genomes to Fields Initiative, PS Schnable (2020) Data-driven identification of environmental variables influencing phenotypic plasticity to facilitate breeding for future climates: a case study involving grain yield of hybrid maize. SSRN, 3684755. doi:10.2139/ssrn.3684755

[ Abstract | 2 October 2020 ]
Miao C, Y Xu, S Liu, PS Schnable, JC Schnable (2020) Functional principal component based time-series genome-wide association in sorghum. bioRχiv, 2020.02.16.951467. doi:10.1101/2020.02.16.951467

[ Abstract | Full Text PDF (External) | Supplemental Materials | 17 February 2020 ]

2019 (1 article)Top ⇪

Li Z, SB Tirado, DC Kadam, L Coffey, ND Miller, EP Spalding, AJ Lorenz, N de Leon, SM Kaeppler, PS Schnable, NM Springer, CN Hirsch (2019) Characterizing allele-by-environment interactions using maize introgression lines. bioRχiv, 738070. doi:10.1101/738070

[ Abstract | Full Text PDF (External) | Supplemental Materials | 16 August 2019 ]

2018 (1 article)Top ⇪

Li L, Y Du, C He, CR Dietrich, J Li, X Ma, R Wang, Q Liu, S Liu, G Wang, PS Schnable, J Zheng (2018) A novel maize gene, glossy6 involved in epicuticular wax deposition and drought tolerance. bioRχiv, 378687. doi:10.1101/378687

[ Abstract | Full Text PDF (External) | Supplemental Materials | 27 July 2018 ]

2017 (1 article)Top ⇪

Kamruzzaman M, A Kalyanaraman, B Krishnamoorthy, PS Schnable (2017) Toward a scalable exploratory framework for complex high-dimensional phenomics data. bioRχiv, 159954. doi:10.1101/159954

[ Abstract | Full Text PDF (External) | Supplemental Materials | 5 July 2017 ]

2016 (1 article)Top ⇪

Chawla V, HS Naik, D Hayes, PS Schnable, B Ganapathysubramanian, S Sarkar (2016) A bayesian network approach to county-level corn yield prediction using historical data and expert knowledge. arXiv, 1608.05127v1. doi:10.1145/1235 (In Proceedings of the 22nd ACM SIGKDD Workshop on Data Science for Food, Energy and Water, 2016 - San Francisco, CA, USA)

[ Abstract | Full Text PDF (External) | 17 August 2016 ]