Recursion Pharmaceuticals, Inc.

Safiye Celik

Berton Allen Earnshaw

James Douglas Jensen

Conor Austin Forsman Tillinghast

Marta Marie Fay

James Benjamin Taylor

Kyle Rollins Hansen

Jacob Carter Cooper

Seyhmus Guler

Peter Foster McLean

Renat Nailevich Khaliullin

Nathan Henry Lazar

Maryam Fallah

Imran Saeedul Haque

Mohammadsadegh Saberian

Ayla Yasmin Khan

Jessica Wai Yin Leung

Oren Zeev Kraus

Chi Cheng

Benjamin John Mabey

Kristen Rose Morse

Maureen Katherine Makes

Vasudev Sharma

Dominique Beaini

Kian Runnels Kenyon-Dean

Maciej Sypetkowski

Jaichitra Balakrishnan

Mason L Victors

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

Tongzhou Shen

Andrew David Blevins

Shweta Deepchand Bhandare

Lina Maria Nilsson

Denton Hallar Greenfield

Michael Haines

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 training and utilizing generative machine learning models to generate embeddings from phenomic images (or other microscopy representations). For example, the disclosed systems can train a generative machine learning model (e.g., a masked autoencoder generative model) to generate predicted (or reconstructed) phenomic images from masked version of ground truth training phenomic images. In some cases, the disclosed systems utilize a momentum-tracking optimizer while reducing a loss of the generative machine learning model to enable efficient training on large scale training image batches. Furthermore, the disclosed systems can utilize Fourier transformation losses with multi-stage weighting to improve the accuracy of the generative machine learning model on the phenomic images during training. Indeed, the disclosed systems can utilize the trained generative machine learning model to generate phenomic embeddings from input phenomic images (for various phenomic comparisons).

Utilizing masked autoencoder generative models to extract microscopy representation autoencoder embeddings

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

Sudbury, MA, United States of America

Safiye Celik