Insitro, Inc.

Jeevaa Velayutham

Shengjiang Tu

Zachary Phillips

Hervé Marie-Nelly

Christopher Probert

Haoyang Zeng

Herve Marie-nelly

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.

Biological image transformation using machine-learning models

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.

Autonomous cell imaging and modeling system

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.

Machine-learning-enabled imputation of spatial omics data based on histopathology image data

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.

Growing community of inventors

Puchong, Malaysia

Jeevaa Velayutham