Abstract: Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.

Abstract: Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.

Abstract: The present disclosure relates generally to machine learning techniques, and more specifically to machine learning techniques for generating synthetic spatial omics data based on histopathology image data. An exemplary system for generating synthetic spatial omics images comprises: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving a histopathology image depicting a diseased region of interest of an input tissue sample; and generating a synthetic spatial omics image depicting one or more stained structures of interest within the diseased region of interest by inputting the histopathology image into a generator of a trained generative adversarial network (GAN) model.

Abstract: The present disclosure relates generally to providing a cellular time-series imaging, modeling, and analysis platform, and more specifically to acquiring time-series image data and using various machine learning models to model and analyze subcellular particle movements and changes in cellular positional and morphological characteristics using unsupervised embedding generation. The platform can be applied to evaluate various cellular and subcellular processes by generating summary embeddings of time-series image data that enable analysis of dynamic cellular and subcellular processes over time (e.g., the movement of particles within a cell, neurites on developing neurons, etc.) for enhanced identification of differences between cell states (e.g., between sick and healthy cells) and generation of disease models which can be used to analyze the impact of various therapeutic interventions, among other improvements described throughout.

Abstract: An exemplary discovery platform includes machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method of identifying a patient subgroup of interest, comprises inputting a plurality of medical images obtained from a group of clinical subjects into a trained unsupervised machine-learning model to obtain a plurality of embeddings in a latent space, clustering the plurality of embeddings to generate one or more clusters of embeddings, identifying one or more patient subgroups corresponding to the one or more clusters of embeddings, and associating each patient subgroup of the one or more patient subgroups with a covariant to identify the patient subgroup of interest.

Abstract: The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method identifying a covariant of interest with respect to drug response phenotype (DRP) of a treatment is disclosed.

Abstract: The present disclosure relates generally to an autonomous cell imaging and modeling platform, and more specifically to machine-learning techniques for using microscopy imaging data to continuously study live biological cells. The autonomous cell imaging and modeling platform can be applied to evaluate various cellular processes, such as cellular differentiation, optimization of cell culture (e.g., in-plate cytometry), disease modeling, histopathology imaging, and genetic and chemical screening, using a dynamic universal imaging system. In some embodiments, the platform comprises a set of label-free computational imaging techniques, self-supervised learning models, and robotic devices configured in an autonomous imaging system to study positional and morphological characteristics in particular cellular substructures of a cell culture in an efficient and non-destructive manner over time.

Abstract: The present disclosure relates generally to an autonomous cell imaging and modeling platform, and more specifically to machine-learning techniques for using microscopy imaging data to continuously study live biological cells. The autonomous cell imaging and modeling platform can be applied to evaluate various cellular processes, such as cellular differentiation, optimization of cell culture (e.g., in-plate cytometry), disease modeling, histopathology imaging, and genetic and chemical screening, using a dynamic universal imaging system. In some embodiments, the platform comprises a set of label-free computational imaging techniques, self-supervised learning models, and robotic devices configured in an autonomous imaging system to study positional and morphological characteristics in particular cellular substructures of a cell culture in an efficient and non-destructive manner over time.

Abstract: Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to: compare first nodes of a first call graph to second nodes of a second call graph based, at least in part, on hash values associated with the first and second nodes to identify one or more of the second nodes that are absent from the first nodes.

Abstract: Disclosed herein are methods for performing in situ sequencing of RNA transcripts with non-uniform 5? ends. During reverse transcription (RT) of RNA transcripts, RT enzyme is induced to “template-switch” to a separate oligonucleotide provided as the template for the upstream flanking region. This flanking region is grafted onto the beginning of the cDNA, enabling padlock probe detection, rolling circle amplification, and fluorescent in situ sequencing. Overall, the disclosed method for in situ sequencing can be applicable for analyzing exogenously introduced transcripts (e.g., identifying and determining impact of a perturbation including a CRISPR perturbation or shRNA/siRNA/ASO perturbation), analyzing naturally occurring transcripts (e.g., measuring gene expression, detecting splicing events), and analyzing modified, naturally occurring transcripts (e.g., detecting mutations or gene edits).

Abstract: An exemplary method for predicting one or more adipose depots for a patient includes receiving one or more Dual-energy X-ray Absorptiometry (DEXA) scans comprising at least a portion of a torso of the patient; providing at least one or more portions of the one or more DEXA scans to a trained machine-learning model, wherein the machine-learning model is trained using a training dataset comprising: a plurality of training DEXA scans of a plurality of subjects and a plurality of corresponding Magnetic Resonance Imaging (MRI)-image-based adiposity scores of the plurality of subjects; and predicting the one or more adipose depots for the patient utilizing the trained machine-learning model.

Abstract: The present disclosure relates generally to providing a cellular time-series imaging, modeling, and analysis platform, and more specifically to acquiring time-series image data and using various machine learning models to model and analyze subcellular particle movements and changes in cellular positional and morphological characteristics using unsupervised embedding generation. The platform can be applied to evaluate various cellular and subcellular processes by generating summary embeddings of time-series image data that enable analysis of dynamic cellular and subcellular processes over time (e.g., the movement of particles within a cell, neurites on developing neurons, etc.) for enhanced identification of differences between cell states (e.g., between sick and healthy cells) and generation of disease models which can be used to analyze the impact of various therapeutic interventions, among other improvements described throughout.

Abstract: The present disclosure relates generally to providing a cellular time-series imaging, modeling, and analysis platform, and more specifically to acquiring time-series image data and using various machine learning models to model and analyze subcellular particle movements and changes in cellular positional and morphological characteristics using unsupervised embedding generation. The platform can be applied to evaluate various cellular and subcellular processes by generating summary embeddings of time-series image data that enable analysis of dynamic cellular and subcellular processes over time (e.g., the movement of particles within a cell, neurites on developing neurons, etc.) for enhanced identification of differences between cell states (e.g., between sick and healthy cells) and generation of disease models which can be used to analyze the impact of various therapeutic interventions, among other improvements described throughout.

Abstract: Provided herein are methods of pooled screening of cells from different genetic backgrounds. Also provided herein are computer-implemented methods for aligning between a first plurality of images and a second plurality of images of biological samples.

Abstract: The present disclosure relates to an autonomous system for maintaining and differentiating induced pluripotency cells (iPSCs) based on quality and confluence conditions using machine learning, to obtain differentiated cells for phenotypic analyses and/or other cellular assays.

Abstract: The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method of identifying a patient subgroup of interest, comprising: inputting a plurality of medical images obtained from a group of clinical subjects into a trained unsupervised machine-learning model to obtain a plurality of embeddings in a latent space; clustering the plurality of embeddings to generate one or more clusters of embeddings; identifying one or more patient subgroups corresponding to the one or more clusters of embeddings; and associating each patient subgroup of the one or more patient subgroups with a covariant to identify the patient subgroup of interest.

Abstract: The present disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of the standard-of-care (SoC), which can be used to identify a relevant patient population for a therapeutic with a known mechanism of action (MoA). An exemplary method for predicting activity of a molecular analyte of a patient comprises: training a first module of a machine learning model based on a plurality of medical images of a first cohort; training a second module of the machine learning model based on one or more molecular analyte data sets obtained from a second cohort; receiving a medical image from the patient; and predicting, using the trained first and second modules of the machine learning model, the activity of the molecular analyte from the medical image of the patient.

Abstract: The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method identifying a covariant of interest with respect to drug response phenotype (DRP) of a treatment is disclosed.

Abstract: The present disclosure relates to methods of pooled optical screening of genetically barcoded cells comprising genetic perturbations, and simultaneous transcriptional measurements.

Abstract: Embodiments of the disclosure include methods for implementing a predictive model that predicts pluripotency of cells through a cost efficient and non-destructive means. The predictive model analyzes contrast images captured from the cells and outputs predictions of cellular pluripotency at the cellular level. Thus, implementation of the predictive model guides the selection and isolation of cells that are predicted to be pluripotent. Furthermore, the predictive model facilitates retrospective analyses to correlate pluripotency metrics with differentiation success and further enables tracking of cellular pluripotency over time (e.g., to evaluate differentiation of cells).