Creating interpretable deep learning models to identify species using environmental DNA sequences

Monitoring species' presence in an ecosystem is crucial for conservation and understanding habitat diversity, but can be expensive and time consuming. As a result, ecologists have begun using the DNA that animals naturally leave behind in water or soil (called environmental DNA, or eDNA) to identify the species present in an environment. Recent work has shown that when used to identify species, convolutional neural networks (CNNs) can be as much as 150 times faster than ObiTools, a traditional method that does not use deep learning. However, CNNs are black boxes, meaning it is impossible to "fact check" why they predict that a given sequence belongs to a particular species. In this work, we introduce an interpretable, prototype-based CNN using the ProtoPNet framework that surpasses previous accuracy on a challenging eDNA dataset. The network is able to visualize the sequences of bases that are most distinctive for each species in the dataset, and introduces a novel skip connection that improves the interpretability of the original ProtoPNet. Our results show that reducing reliance on the convolutional output increases both interpretability and accuracy.