Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a phenomap and germline data to generate a measure of target discovery power and gene targets for trait of interest. Indeed, the disclosed systems can sample a test subset of genomic patient data samples from a combined set of data corresponding to a trait of interest. For instance, the disclosed systems identify a test gene target from the sampled test subset that satisfies a threshold correlation with the trait of interest. In some instances, the disclosed systems generate a test phenomap gene target by comparing a test gene target in the digital phenomap with additional genes in the digital phenomap. Moreover, the disclosed systems can generate a measure of target discovery power of the digital phenomap by comparing the test phenomap gene target with a gene target set identified from the combined set.

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.