Virginia Tech Assistant Professor of Computer Science Anuj Karpatne has been awarded a National Science Foundation (NSF) grant entitled "Biology‐guided neural networks for discovering phenotypic traits", projected to run for two years. 

This $216K project is part of the NSF's Harnessing the Data Revolution (HDR) Big Idea activity, and is jointly supported by the HDR and the Division of Biological Infrastructure within the NSF Directorate of Directorate for Biological Sciences.

This HDR project is in collaboration with colleagues at University of South Dakota, Drexel University, Tulane University, and Seattle Children's Hospital. This convergent research will accelerate scientific discovery across the biological sciences and computer science by harnessing the data revolution in conjunction with biological knowledge.

This project will use advances in machine learning and machine-readable biological knowledge to create a new method to automatically identify traits from images of organisms.  It will leverage advances in state-of-the-art machine learning to develop a novel class of artificial neural networks that can exploit the machine readable and predictive knowledge about biology that is available in the form of phylogenies and anatomy ontologies.

These biology-guided neural networks are expected to automatically detect and predict traits from specimen images, with little training data. Image-based trait data derived from this work will enable progress in gene-phenotype mapping to novel traits and understanding patterns of evolution. 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. These will broadly impact the many domains that will adopt machine learning as a way to make discoveries from images.