Verily Life Sciences LLC

Google Inc.

Ryan Poplin

Mark Andrew DePristo

Martin Christian Stumpe

Marc Adlai Coram

Philip Charles Nelson

Peter Junteng Liu

Dale R Webster

Lily Hao Yi Peng

Avinash Vaidyanathan Varadarajan

Arunachalam Narayanaswamy

Yun Liu

Varun Gulshan

Derek Janme Wu

Katharine Blumer

Erin Hoffman-John

Mauricio Carneiro

Adam Prins

Richard Wu

Andeep Toor

Joshua Vincent Dillon

Emily Amanda Fertig

Mark Andrew Depristo

A method for detecting false positive variant calls in a next generation sequencing analysis pipeline involves obtaining a plurality of read pileup windows associated with a first sample genome. The method also involves obtaining, for each reference nucleotide position represented in the plurality of read pileup windows, a label indicating that the reference nucleotide position is either (i) a known variant or (ii) a non-variant. The method further involves training a neural network based on data indicative of the plurality of read pileup windows and the labels. Additionally, the method involves receiving a read pileup window associated with a second sample genome. Further, the method involves determining, using the trained neural network, a likelihood that the read pileup window associated with the second sample genome represents a variant.

Detecting false positive variant calls in next-generation sequencing

Systems and methods are provided for receiving at least one image and a reference image, and performing a plurality of downscaling operations having separable convolutions on the received at least one image. A plurality of residual blocks may be formed, with each residual block containing two separable convolutions of the kernel and two instance normalizations. A plurality of upscaling operations may be performed on the plurality of residual blocks, and a stylized image may be displayed based on at least the performed plurality of upscaling operations and the reference image.

High resolution real-time artistic style transfer pipeline

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

The present invention relates to systems and methods for determining the identity of alleles from genomic sequencing data via pseudoalignments. Particularly, aspects of the present invention are directed to a computer implemented method that includes obtaining a paired-end fragment from a genomic sample, determining a first nucleotide substring from a first end of the paired-end fragment matches a nucleotide substring within an index of nucleotide substrings and alleles, determining a second nucleotide substring from a second end of the paired-end fragment matches another nucleotide substring within the index, determining an intersection between the nucleotide substring and the another nucleotide substring exists, when an allele that contains the nucleotide substring is the same allele that contains the another nucleotide substring; and determining a probability that the paired end fragment is an observation of the allele based on the existence of the intersection.

Systems and methods for determining the identity of alleles from genomic sequencing data

A method for variant calling in a next generation sequencing analysis pipeline involves obtaining a plurality of sequence reads that each include a nucleotide aligned at a nucleotide position within a sample genome. The method also involves obtaining a plurality of alleles associated with the nucleotide position. The method further involves determining that a particular allele of the plurality of alleles matches one or more sequence reads of the plurality of sequence reads, wherein the particular allele is located at the nucleotide position. Additionally, the method involves generating an image based on information associated with the plurality of sequence reads. Further, the method involves determining, by providing the generated image to a trained neural network, a likelihood that the sample genome contains the particular allele. The method may also involves providing an output signal indicative of the determined likelihood.

Deep learning analysis pipeline for next generation sequencing

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Palo Alto, CA, United States of America

Ryan Poplin