IP Library › Granted Patent US 12,380,964
Granted Patent B2
US 12,380,964 · App. 18/240,489 · Granted Aug 5, 2025

Convolutional neural network systems and methods for data classification

Inventors: Virgil Nicula (Cupertino, CA); Anton Valouev (Palo Alto, CA); Darya Filippova (Sunnyvale, CA); Matthew H. Larson (San Francisco, CA); M. Cyrus Maher (San Mateo, CA); Monica Portela dos Santos Pimentel (San Jose, CA); Robert Abe Paine Calef (Redwood City, CA); Collin Melton (Menlo Park, CA)
Assignee: GRAIL, Inc.
G16B30/10G06N3/04G06N3/084G16B40/20G16B40/30G16H50/20
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Quick Facts
Patent No.
US 12,380,964
App. No.
18/240,489
Granted
Aug 5, 2025
Kind
B2
Abstract

Classification of cancer condition, in a plurality of different cancer conditions, for a species, is provided in which, for each training subject in a plurality of training subjects, there is obtained a cancer condition and a genotypic data construct including genotypic information for the respective training subject. Genotypic constructs are formatted into corresponding vector sets comprising one or more vectors. Vector sets are provided to a network architecture including a convolutional neural network path comprising at least a first convolutional layer associated with a first filter that comprise a first set of filter weights and a scorer. Scores, corresponding to the input of vector sets into the network architecture, are obtained from the scorer. Comparison of respective scores to the corresponding cancer condition of the corresponding training subjects is used to adjust the filter weights thereby training the network architecture to classify cancer condition.

Claims (50)

1. A method of diagnosing a disease state in a test subject by using a trained artificial neural network, the method comprising:

obtaining, from a biological sample associated with the test subject, sequencing data derived from a methylation sequencing assay of cell-free nucleic acids in the biological sample;

applying, subsequent to the obtaining, the sequencing data to the trained artificial neural network;

determining, subsequent to the applying and by processing the sequencing data using a plurality of filter sets resident within a plurality of convolutional layers of the trained artificial neural network, whether a methylation profile of the sequencing data is detected that is indicative of a disease state, wherein:

a first filter of the plurality of filter sets is configured to identify a methylation pattern at a single methylation site in a genomic region;

a second filter of the plurality of filter sets is configured to identify a relationship between each of the single methylation sites in the genomic region; and

wherein the trained artificial neural network is configured to integrate outputs from the first filter and outputs from the second filter to generate the methylation profile; and

providing, on a display screen of a computing device and based on the determining, a diagnosis for the test subject with respect to the disease state.

2. The method of claim 1 , wherein the disease state is cancer.

3. The method of claim 1 , wherein the providing the diagnosis comprises classifying the test subject as having the disease state responsive to determining that the methylation profile of the sequencing data is indicative of the disease state.

4. The method of claim 1 , wherein the providing the diagnosis comprises classifying the test subject as not having the disease state responsive to determining that the methylation profile of the sequencing data is not indicative of the disease state.

5. The method of claim 1 , wherein the processing the sequencing data comprises generating a first classification score, wherein the first classification score is a binary classification score.

6. The method of claim 5 , further comprising:

introducing the first classification score and a second classification score generated by a second trained classification model as input to a third trained classification model;

receiving, from the third trained classification model, a third classification score; and

generating, based on the third classification score, another diagnosis for the test subject with respect to the disease state.

7. The method of claim 6 , wherein the second trained classification model is selected from the group consisting of: an M-score classifier, a lengths classifier, a B-score classifier, and an allelic ratio classifier.

8. The method of claim 6 , wherein the third trained classification model is a logistic regression model.

9. The method of claim 1 , wherein the methylation profile comprises at least one of: a methylation index of a CpG site, a methylation density of CpG sites in a region, a distribution of the CpG sites over a contiguous region, a pattern or level of methylation for each individual CpG site within the region, and non-CpG methylation information.

10. The method of claim 1 , further comprising generating a graph that displays results associated with the diagnosis.

11. A computer system for diagnosing a disease state in a test subject by using a trained artificial neural network, the computer system comprising:

at least one processor;

a graphical processing unit having a graphical processing memory configured to store a network architecture; and

a memory, the memory storing at least one program for execution by the at least one processor, the at least one program comprising instructions for:

obtaining, from a biological sample associated with the test subject, sequencing data derived from a methylation sequencing assay of cell-free nucleic acids in the biological sample;

applying, subsequent to the obtaining, the sequencing data to the trained artificial neural network;

determining, subsequent to the applying and by processing the sequencing data using a plurality of filter sets resident within a plurality of convolutional layers of the trained artificial neural network, whether a methylation profile of the sequencing data is detected that is indicative of a disease state, wherein:

a first filter of the plurality of filter sets is configured to identify a methylation pattern at a single methylation site in a genomic region;

a second filter of the plurality of filter sets is configured to identify a relationship between each of the single methylation sites in the genomic region; and

wherein the trained artificial neural network is configured to integrate outputs from the first filter and outputs from the second filter to generate the methylation profile; and

providing, on a display screen of a computing device associated with the system and based on the determining, a diagnosis for the test subject with respect to the disease state.

12. The computer system of claim 11 , wherein the instructions for providing the diagnosis comprise instructions for classifying the test subject as having the disease state responsive to determining that the methylation profile of the sequencing data is indicative of the disease state.

13. The computer system of claim 11 , wherein the instructions for providing the diagnosis comprise instructions for classifying the test subject as not having the disease state responsive to determining that the methylation profile of the sequencing data is not indicative of the disease state.

14. The computer system of claim 11 , wherein the instructions for processing the sequencing data comprise instructions for generating a first classification score, wherein the first classification score is a binary classification score.

15. The computer system of claim 14 , wherein the instructions further comprise:

introducing the first classification score and a second classification score generated by a second trained classification model as input to a third trained classification model;

receiving, from the third trained classification model, a third classification score; and

generating, based on the third classification score, another diagnosis for the test subject with respect to the disease state.

16. The computer system of claim 15 , wherein the second trained classification model is selected from the group consisting of: an M-score classifier, a lengths classifier, a B-score classifier, and an allelic ratio classifier.

17. The computer system of claim 15 , wherein the third trained classification model is a logistic regression model.

18. The computer system of claim 11 , wherein the methylation profile comprises at least one of: a methylation index of a CpG site, a methylation density of CpG sites in a region, a distribution of the CpG sites over a contiguous region, a pattern or level of methylation for each individual CpG site within the region, and non-CpG methylation information.

19. The computer system of claim 11 , further comprising generating a graph that displays results associated with the diagnosis.

20. A non-transitory computer-readable storage medium storing computer-executable instructions which, when executed by a processor, cause the processor to perform operations comprising:

obtaining, from a biological sample associated with a test subject, sequencing data derived from a methylation sequencing assay of cell-free nucleic acids in a biological sample;

applying, subsequent to the obtaining, the sequencing data to a trained artificial neural network;

determining, subsequent to the applying and by processing the sequencing data using a plurality of filter sets resident within a plurality of convolutional layers of the trained artificial neural network, whether a methylation profile of the sequencing data is detected that is indicative of a disease state, wherein:

a first filter of the plurality of filter sets is configured to identify a methylation pattern at a single methylation site in a genomic region;

a second filter of the plurality of filter sets is configured to identify a relationship between each of the single methylation sites in the genomic region; and

wherein the trained artificial neural network is configured to integrate outputs from the first filter and outputs from the second filter to generate the methylation profile; and

providing, on a display screen of a computing device and based on the determining, a diagnosis for the test subject with respect to the disease state.

Assignments (3)
CHANGE OF NAME Recorded Feb 13, 2025
From: GRAIL, LLC
To: GRAIL, INC.
Reel/Frame 070208/0943 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 6, 2023
From: NICULA, VIRGIL; VALOUEV, ANTON; FILIPPOVA, DARYA; LARSON, MATTHEW H.; MAHER, M. CYRUS; PORTELA DOS SANTOS PIMENTEL, MONICA; CALEF, ROBERT ABE PAINE; MELTON, COLLIN
To: GRAIL, INC.
Reel/Frame 064817/0347 →
MERGER AND CHANGE OF NAME Recorded Sep 6, 2023
From: GRAIL, INC.; SDG OPS, LLC
To: GRAIL, LLC
Reel/Frame 064817/0483 →
Continuity (4)
Continuation 17936529 · Sep 29, 2022
Continuation 16428575 · May 31, 2019
Provisional Application 62679746 · Jun 1, 2018
Related Publication 20240062849A1 · Feb 22, 2024
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