Utilizing machine learning and digital embedding processes to generate digital maps of biology and user interfaces for evaluating map efficacy

A system comprising:

• at least one processor;
• and
• at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
• receive, from a client device, a similarity request for two or more cell perturbations;
• generate similarity measures between the two or more cell perturbations by comparing aggregated embeddings corresponding to the two or more cell perturbations, wherein the aggregated embeddings are aggregated from perturbation experiment unit embeddings generated utilizing a machine learning model from perturbation data;
• and
• in response to the similarity request, provide, for display via the client device, a perturbation similarity map reflecting the similarity measures between the two or more cell perturbations.

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computing device to:

• receive, from a client device, a similarity request for two or more cell perturbations;
• generate similarity measures between the two or more cell perturbations by comparing aggregated embeddings corresponding to the two or more cell perturbations, wherein the aggregated embeddings are aggregated from perturbation experiment unit embeddings generated utilizing a machine learning model from perturbation data;
• and
• in response to the similarity request, provide, for display via the client device, a perturbation similarity map reflecting the similarity measures between the two or more cell perturbations.