The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Sep. 03, 2024
Filed:
Dec. 21, 2023
Recursion Pharmaceuticals, Inc., Salt Lake City, UT (US);
Nathan Henry Lazar, Salt Lake City, UT (US);
Conor Austin Forsman Tillinghast, Salt Lake City, UT (US);
James Douglas Jensen, Farmington, UT (US);
James Benjamin Taylor, Midlothian, VA (US);
Berton Allen Earnshaw, Cedar Hills, UT (US);
Marta Marie Fay, Salt Lake City, UT (US);
Renat Nailevich Khaliullin, Salt Lake City, UT (US);
Jacob Carter Cooper, Sandy, UT (US);
Imran Saeedul Haque, Salt Lake City, UT (US);
Seyhmus Guler, Salt Lake City, UT (US);
Kyle Rollins Hansen, Kaysville, UT (US);
Safiye Celik, Sudbury, MA (US);
Recursion Pharmaceuticals, Inc., Salt Lake City, UT (US);
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.