Veracyte, Inc.

Genentech, Inc.

Yoonha Choi

Giulia C Kennedy

Jing Huang

Moraima Pagan

Joseph Nathaniel Paulson

Tina Geritz Nielsen

James Diggans

Edward Jean Bataillard

Carl Wilson Harris, Iii

Su Yeon Kim

Daniel Jay Schneider

Carsten Henneges

Joshua Babiarz

Patric Sean Walsh

Daniel Pankratz

Systems and methods described herein improve outcomes of subjects with lymphoma. Subjects with lymphoma may be able to avoid conventional treatments that have a high probability of leading to an adverse effect in the subject. Systems and methods allow for a more accurate prediction of subjects who cannot tolerate a particular treatment. Methods may include accessing an input data set that includes multiple input data values pertaining to a particular subject with lymphoma. The method may further include inputting the input data set into a machine-learning model to generate a score corresponding to the degree to which the particular subject will tolerate a particular treatment. The method may include outputting a prediction of the tolerance of the particular subject to the particular treatment using the generated score.

Predicting tolerability in aggressive non-hodgkin lymphoma

The present disclosure provides systems, methods, and classifiers for differentiating between samples as usual interstitial pneumonia (UIP) or non-UIP.

Methods and systems for detecting usual interstitial pneumonia

The present invention provides methods for sequencing and analysis of nucleic acids and determining that a subject is positive for a non-usual interstitial pneumonia subtype.

Methods for nucleic acid sequencing

The methods disclosed herein can be used to determine sample integrity, such as sample identity, by using kinship coefficients. Kinship coefficients between two samples can be determined by measuring genetic relatedness in order to determine whether the samples are related or not related.

Methods and systems for determining biological sample integrity

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South San Francisco, CA, United States of America

Yoonha Choi