Unlike genetic data, the phenotypes, or traits of organisms such as their visible features, are not available in databases for analysis. The lack of machine-readable trait data has slowed progress on four grand challenge problems in biology: predicting the genes that generate traits, understanding the patterns of evolution, predicting the effects of ecological change, and species identification. The BGNN project aims to leverage advances in state-of-the-art machine learning to develop a novel class of artificial neural networks, termed biology-guided neural networks (BGNNs), that can exploit the machine readable and predictive knowledge about biology that is available in the form of phylogenies and anatomy ontologies. These BGNNs are expected to automatically detect and predict traits from specimen images, with little training data. Currently the project is focusing on teleost fishes because of many high-quality data resources available (digital images, evolutionary trees, anatomy ontology). The resulting machine learning model can be generalized to other disciplines that have formally structured knowledge, and will contribute to advances in computer science by going beyond black-box learning and making important advances toward Explainable Artificial Intelligence. Image-based trait data derived from this work will enable progress in gene-phenotype mapping to novel traits and understanding patterns of evolution. It may be extended to applied areas, such as agriculture or the biomedical domain. This convergent research will accelerate scientific discovery across the biological sciences and computer science by harnessing the data revolution in conjunction with biological knowledge.