Google Inc.

Marc Berndl

Patrick F Riley

Philip Charles Nelson

Michelle Therese Hoerner Dimon

Michael Alex Frumkin

Eric Martin Christiansen

Dale M Ando

Marc Adlai Coram

Dan Liu

Shirley Jing Shao

Annalisa Marie Pawlosky

Brian Trippe

Diane L Tang

Adam Isaac Juda

Michael L Gibbons

Karthik Gopalratnam

Ali Bashir

Lauren Schiff

Jonathan G Alferness

Sridhar Ramaswamy

Myles A Sussman

Nicholas Charles Fox

Gagan Aggarwal

Josh T Wills

Amir Najmi

Levent Ertöz

Humphrey H Nash, Jr

Zachary Cutts

Alexander Julian Tran

Derek Leslie-Cook

Lusann Yang

Diana Terri Wu

Michelle Dimon

Sara Ahadi

Anna Le

Jun Kim

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for obtaining data defining a sequence for an aptamer, the aptamer comprising a string of nucleobases; encoding the data defining the sequence for the aptamer as a neural network input; and processing the neural network input using a neural network to generate an output that characterizes how strongly the aptamer binds to a particular target molecule, wherein the neural network has been configured through training to receive the data defining the sequence and to process the data to generate predicted outputs that characterize how strongly the aptamer binds to the particular target molecule.

Neural network for processing aptamer data

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving graph data representing an input graph comprising a plurality of vertices connected by edges; generating, from the graph data, vertex input data representing characteristics of each vertex in the input graph and pair input data representing characteristics of pairs of vertices in the input graph; and generating order-invariant features of the input graph using a neural network, wherein the neural network comprises: a first subnetwork configured to generate a first alternative representation of the vertex input data and a first alternative representation of the pair input data from the vertex input data and the pair input data; and a combining layer configured to receive an input alternative representation and to process the input alternative representation to generate the order-invariant features.

Neural network for processing graph data

This disclosure describes methods and compositions for protein and peptide sequencing.

Methods and compositions for protein and peptide sequencing

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing cell images using neural networks. One of the methods includes obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images.

Processing cell images using neural networks

The present disclosure relates to phenotype analysis of cellular image data using a deep metric network. One example embodiment includes a method. The method includes receiving a target image of a target biological cell having a target phenotype. The method also includes obtaining a semantic embedding associated with the target image. The semantic embedding is generated using a machine-learned, deep metric network model. Further, the method includes obtaining, for each of a plurality of candidate images of candidate biological cells each having a respective candidate phenotype, a semantic embedding associated with the respective candidate image. In addition, the method includes identifying, for each of the semantic embeddings, common morphological variations and reducing, for each of the semantic embeddings based on the identified common morphological variations, effects of nuisances. Even further, the method includes determining, by the computing device, a similarity score for each candidate image.

Phenotype analysis of cellular image data using a deep metric network

The present disclosure relates to analysis of perturbed subjects using semantic embeddings. One example embodiment includes a method. The method includes applying a respective perturbation to each of a plurality of subjects in a controlled environment. The method also includes producing a respective visual representation for each of the perturbed subjects using at least one imaging modality. Further, the method includes obtaining, by a computing device for each of the respective visual representations, a corresponding semantic embedding associated with the respective visual representation. The semantic embedding associated with the respective visual representation is generated using a machine-learned, deep metric network model. In addition, the method includes classifying, by the computing device based on the corresponding semantic embedding, each of the visual representations into one or more groups.

Analysis of perturbed subjects using semantic embeddings

The present disclosure relates to a phenotype analysis of cellular image data using a deep metric network. One example embodiment includes a method. The method includes receiving, by a computing device, a plurality of candidate images of candidate biological cells each having a respective candidate phenotype. The method also includes obtaining, by the computing device for each of the plurality of candidate images, a semantic embedding associated with the respective candidate image. Further, the method includes grouping, by the computing device, the plurality of candidate images into a plurality of phenotypic strata based on their respective semantic embeddings.

Growing community of inventors

San Francisco, CA, United States of America

Marc Berndl