Google Inc.

Pacific Biosciences of California, Inc.

Dale R Webster

Lily Hao Yi Peng

Avinash Vaidyanathan Varadarajan

Stephen Wilson Turner

Jonas Korlach

Jeremiah Hanes

Kevin Travers

Jon M Sorenson

Benjamin A Flusberg

John Lyle

Cheryl Heiner

Patrick Marks

Lei Jia

Austin B Tomaney

Andrey Kislyuk

Jessica Lee

Jeffrey Wegener

Martin Christian Stumpe

Marc Adlai Coram

Tyson A Clark

Igor D Vilfan

Philip Charles Nelson

Christopher Semturs

Ryan Poplin

Arunachalam Narayanaswamy

Yun Liu

Joseph Puglisi

Katharine Blumer

Derek Janme Wu

Varun Gulshan

Akinori Mitani

Pinal Bavishi

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a fundus image processing machine learning models that is configured to process one or more fundus images captured by a fundus camera to generate a predicted label. One of the methods includes generating training data, comprising: receiving sets of one or more training fundus images captured by a fundus camera; receiving, for each of the sets, a ground truth label assigned to a different image of the eye of the patient corresponding to the set that has been captured using a different imaging modality; and generating, for each set of training fundus images, a training example that includes the set of training fundus images in association with the ground truth label assigned to the different image of the patients eye; and training the machine learning model on the training examples in the training data.

Processing fundus camera images using machine learning models trained using other modalities

Methods, compositions, and systems are provided for characterization of modified nucleic acids. In certain preferred embodiments, single molecule sequencing methods are provided for identification of modified nucleotides within nucleic acid sequences. Modifications detectable by the methods provided herein include chemically modified bases, enzymatically modified bases, abasic sites, non-natural bases, secondary structures, and agents bound to a template nucleic acid.

Classification of nucleic acid templates

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing fundus images using fundus image processing machine learning models.

Processing fundus images using machine learning models to generate blood-related predictions

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing fundus images using fundus image processing machine learning models. One of the methods includes obtaining a model input comprising one or more fundus images, each fundus image being an image of a fundus of an eye of a patient; processing the model input using a fundus image processing machine learning model, wherein the fundus image processing machine learning model is configured to process the model input comprising the one or more fundus image to generate a model output; and processing the model output to generate health analysis data.

Processing fundus images using machine learning models

Identification of nucleic acid template-linked barcodes comprising nucleic acid modifications

Methods for identifying nucleic acid modifications

Growing community of inventors

San Mateo, CA, United States of America

Dale R Webster