Recursion Pharmaceuticals, Inc.

Peter Foster McLean

Berton Allen Earnshaw

Mason L Victors

Blake C Borgeson

Safiye Celik

Nathan Henry Lazar

Renat Nailevich Khaliullin

Maryam Fallah

Mohammadsadegh Saberian

Ayla Yasmin Khan

Jaichitra Balakrishnan

Katie-Rose Skelly

Jessica Wai Yin Leung

Maciej Sypetkowski

Kian Runnels Kenyon-Dean

Oren Zeev Kraus

Chi Cheng

Benjamin John Mabey

Kristen Rose Morse

Maureen Katherine Makes

Vasudev Sharma

Dominique Beaini

Eric Nathaniel Fish

Imran Saeedul Haque

Charles Baker

Benjamin Marc Feder Fogelson

Juan Sebastián Rodríguez Vera

Marissa Saunders

Methods and systems for evaluating a query perturbation, in a cell based assay representing a test state, are provided. Control data points having dimensions representing measurements of different features across control cell aliquots are obtained. Test data points having dimensions representing measurements of different features across test cell aliquots are obtained. A composite test vector is computed between measures of central tendency across the control data points and measures of central tendency across the test data points. Query perturbation data points having dimensions representing measurements of different features across perturbation cell aliquots are obtained. A composite query perturbation vector is computed between measures of central tendency across the control data points and measures of central tendency across the plurality of query perturbation data points.

Systems and methods for evaluating query perturbations

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

In various embodiments, an experiment analysis application detects executional artifacts in experiments involving microwell plates. The experiment analysis application computes one or more sets of spatial features based on one or more heat maps associated with a microwell plate. The experiment analysis application then aggregates the set(s) of spatial features to generate a feature vector. The experiment analysis application inputs the feature vector into a trained classifier. In response, the trained classifier generates a label indicating that the microwell plate is associated with a first executional artifact.

Techniques for analyzing and detecting executional artifacts in microwell plates

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Centerville, UT, United States of America

Peter Foster McLean