Do androids dream of electric sorghum?: Predicting Phenotypes from Multi-Scale Genomic and Environmental Data using Neural Networks and Knowledge Graphs

Abstract

The interplay between an organism's genes, its environment, and the expressed phenotype is dynamic. These interactions within ecosystems are shaped by non-linear multi-scale effects that are difficult to disentangle into discrete components. In the face of anthropogenic climate chance, it is critical to understand environmental and genotypic influences on plant phenotypes and phenophase transitions. However, it is difficult to integrate and interoperate between these datasets. Advances in the fields of ontologies, unsupervised learning, and genomics may overcome the disparate data schema. Here we present a framework to better link phenotypes, environments, and genotypes of plant species across ecosystem scales. This approach utilizing phenotypic data, knowledge graphing, and deep learning, serves as the groundwork for a new scientific sub-discipline: “Computational Ecogenomics”