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

US12079992B1 · kind B1 · utility

Assignee

• Recursion Pharmaceuticals, Inc. · US

Inventors

• Nathan Lazar · Salt Lake City, US
• Conor Austin Forsman Tillinghast · Salt Lake City, US
• James Douglas Jensen · Farmington, US
• James Benjamin Taylor · Midlothian, US
• Berton Earnshaw · Cedar Hills, US
• Marta Marie Fay · Salt Lake City, US
• Renat Khaliullin · Salt Lake City, US
• Jacob Carter Cooper · Sandy, US
• Imran Saeedul Haque · San Francisco, US
• Seyhmus Guler · Salt Lake City, US
• Kyle Rollins Hansen · Kaysville, US
• Safiye Celik · Sudbury, US

Key dates

• Filing date: Dec 21, 2023
• Grant date: Sep 3, 2024
• Expiry date: Dec 21, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G06T2207/30072
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

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. In particular, in one or more embodiments, the disclosed systems receive perturbation data for a plurality of perturbation experiment units corresponding to a plurality of perturbation classes. Further, the systems generate, utilizing a machine learning model, a plurality of perturbation experiment unit embeddings from the perturbation data. Additionally, the systems align, utilizing an alignment model, the plurality of perturbation experiment unit embeddings to generate aligned perturbation unit embeddings. Moreover, the systems aggregate the aligned perturbation unit embeddings to generate aggregated embeddings. Furthermore, the systems generate perturbation comparisons utilizing the perturbation-level embeddings.