Leveraging evolution to engineer biology
with an iterative loop of computational prediction
and experimental validation.
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A quantitative-genetic decomposition of a neural network
We tested equivalent linear mapping (ELM) on a neural network trained to predict phenotypes from genotypes in simulated data. We show that ELM successfully recapitulates additive and epistatic effects learned by the model, even in data with substantial environmental noise.
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From black box to glass box: Making UMAP interpretable with exact feature contributions
We transform UMAP from a black box into a glass box. By learning the embedding function with a certain type of deep network, we can compute equivalent linear mappings of the input features that exactly reconstruct each embedding, revealing the heretofore hidden logic of UMAP.
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Equivalent linear mappings of deep networks are a promising path for biology
Deep networks make accurate predictions, but their nonlinearity makes them a black box, hiding what they have learned. Here, we look inside the black box and analyze the exact relationships they learn for UMAP embeddings and epistasis in a genotype–phenotype dataset.
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