Recursion Pharmaceuticals, Inc.

Kyle Rollins Hansen

Berton Allen Earnshaw

Safiye Celik

James Douglas Jensen

Marta Marie Fay

James Benjamin Taylor

Conor Austin Forsman Tillinghast

Jacob Carter Cooper

Seyhmus Guler

Nathan Henry Lazar

Renat Nailevich Khaliullin

Imran Saeedul Haque

Mason L Victors

Michael Haines

Kristin Ann Clark

Sara Marjean Moore

Lovina Roundy

Ted Douglas Monchamp

Summer Walid Elias

Michael Joseph Uloth

Condie Thomas Swallow, Ii

August Orvis Allen

Eugene Yin-Chung Ting

Sarah Jordan Devore

Shweta Deepchand Bhandare

Tongzhou Shen

Lina Maria Nilsson

Andrew David Blevins

Denton Hallar Greenfield

The present disclosure relates to systems, non-transitory computer-readable media, and methods for embedding perturbation data via a machine learning model and filtering, aligning, and aggregating the embeddings to generate a genome-wide perturbation database for real-time generation of perturbation heatmaps. In particular, in one or more embodiments, the disclosed systems can receive a plurality of perturbation images portraying cells from a plurality of wells corresponding to a plurality of cell perturbations. Further, the systems can generate, utilizing a machine learning model, a plurality of well-level image embeddings from the plurality of perturbation images. Moreover, the systems can align, utilizing an alignment model, the plurality of well-level image embeddings to generate aligned well-level image embeddings. Additionally, the systems can aggregate, according to perturbations of one or more perturbation experiments, the well-level image embeddings to generate perturbation-level image embeddings. Furthermore, the systems can generate perturbation comparisons utilizing the perturbation-level image embeddings.

Utilizing machine learning models to synthesize perturbation data to generate perturbation heatmap graphical user interfaces

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.

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

Growing community of inventors

Kaysville, UT, United States of America

Kyle Rollins Hansen