Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning and digital embedding processes to generate digital maps of biology and user interfaces for evaluating map efficacy. For example, the disclosed systems can generate a combined phenomic-transcriptomic map from embedding perturbation data via a machine learning model and filtering, aligning, aggregating, and relating the embeddings to generate transcriptomic comparisons. Additionally, the disclosed systems can embed phenomic perturbation data via a machine learning model and filtering, aligning, aggregating, and relating the phenomic perturbation embeddings to generate phenomic perturbation comparisons. Furthermore, the disclosed systems can utilize transcriptomic comparisons determined from aggregated transcriptomic embeddings and phenomic embedding comparisons determined from aggregated phenomic perturbation embeddings to generate combined phenomic-transcriptomic maps of biology.

Abstract: Systems and methods that receive as input microscopy images, extract features, and apply layers of processing units to compute one or more sets of cellular phenotype features, particularly antibodies, corresponding to cellular densities and/or fluorescence measured under different conditions. The system is a machine learning architecture having, in one aspect, a deep neural network, typically a convolutional neural network. The deep neural network can be trained and tested directly on raw microscopy images. The system computes class specific feature maps for every phenotype variable using a deep neural network. The system produces predictions for one or more reference antibody variables based on microscopy images within populations of cells.