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README.md

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+![](AgAdapt_Logo.png)
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+<p align="center"><b>Multimodal Data Fusion for Maize Phenotype Prediction across Environments</b></p>
+
+***
+
+# Summary
+
+The AgAdapt algorithm aims to provide multimodal phenotype prediction while using the minimum number of predictor features possible.
+
+A challenging problem in biology is incorporating large-scale data from multiple sources into machine learning models to predict organism traits. We employ deep-learning dimensionality reduction techniques for condensing large data into meaningful predictor variables. Models are then trained using a gradient-boosting regression approach.
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+Our AgAdapt algorithm can serve as a tool for efficient crop production and breeding.
+
+# References
+
+### General References
+
+[1] McFarland, B.A., AlKhalifah, N., Bohn, M. et al. Maize genomes to fields (G2F): 2014–2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets. _BMC Res Notes_ **13**, 71 (2020). https://doi.org/10.1186/s13104-020-4922-8
+
+[2] C J Battey, Gabrielle C Coffing, Andrew D Kern, Visualizing population structure with variational autoencoders, _G3 Genes|Genomes|Genetics_, Volume 11, Issue 1, January 2021, jkaa036, https://doi.org/10.1093/g3journal/jkaa036
+
+### Software and Packages
+
+[1] Peter J. Bradbury, Zhiwu Zhang, Dallas E. Kroon, Terry M. Casstevens, Yogesh Ramdoss, Edward S. Buckler, TASSEL: software for association mapping of complex traits in diverse samples, _Bioinformatics_, Volume 23, Issue 19, 1 October 2007, Pages 2633–2635, https://doi.org/10.1093/bioinformatics/btm308
+
+[2] Jombart T (2008). adegenet: a R package for the multivariate analysis of genetic markers. _Bioinformatics_, **24**, 1403-1405. https://doi.org/10.1093/bioinformatics/btn129
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+[3] Knaus, B.J. and Grünwald, N.J. (2017), vcfR: a package to manipulate and visualize variant call format data in R. Mol Ecol Resour, 17: 44-53. https://doi.org/10.1111/1755-0998.12549