The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 16, 2025

Filed:

Aug. 31, 2018
Applicant:

Grail, Inc., Menlo Park, CA (US);

Inventors:

Ling Shen, Redwood City, CA (US);

Catalin Barbacioru, Fremont, CA (US);

Qinwen Liu, Fremont, CA (US);

Assignee:

GRAIL, Inc., Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16B 30/00 (2019.01); C12Q 1/6869 (2018.01); G06N 20/00 (2019.01); G16B 20/20 (2019.01); G16B 30/10 (2019.01); G16B 40/00 (2019.01); G16B 40/20 (2019.01);
U.S. Cl.
CPC ...
G16B 30/00 (2019.02); C12Q 1/6869 (2013.01); G06N 20/00 (2019.01); G16B 20/20 (2019.02); G16B 40/00 (2019.02); G16B 40/20 (2019.02); G16B 30/10 (2019.02);
Abstract

A system and a method are described for applying a noise model for predicting the occurrence and a level of noise that is present in cfDNA read information. The significance model is trained for a plurality of stratifications of called variants using training data in the stratification. Stratifications may include a partition and a mutation type. The significance model predicts the likelihood of observing a read frequency for a called variant in view of two distributions of the significance model. The first distribution predicts a likelihood of noise occurrence in the sample. The second distribution predicts a likelihood of observing a magnitude of the read frequency for the called variant. The two distributions may further depend on a baseline noise level of blank samples. With these two distributions, the significance model, for a particular stratification, more accurately predicts the likelihood of a false positive for a called variant.


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