US 2020/0005899 A1Published application

Convolutional neural network systems and methods for data classification

AssigneeGrail, LLC

InventorsVirgil Nicula, Anton Valouev, Darya Filippova, Matthew H. Larson, M. Cyrus Maher, Monica Portela dos Santos Pimentel, Robert Abe Paine Calef, Collin Melton

Life of this patent

Estimating the term…

patentgrep

Loading patent…

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.

Claims33

Dependent claims

A computer system for classifying a cancer condition, in a plurality of different cancer conditions, the computer system comprising:

at least one processor;
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:
(A) obtaining, for each respective training subject in a plurality of training subjects of a species:
(i) a cancer condition and (ii) a genotypic data construct that includes genotypic information corresponding to locations of a reference genome of the species, thereby obtaining a plurality of genotypic data constructs;
(B) formatting each genotypic data construct in the plurality of genotypic data constructs into a corresponding vector set comprising one or more corresponding vectors, thereby creating a plurality of vector sets;
(C) providing the plurality of vector sets to a network architecture that includes at least (i) a first convolutional neural network path comprising a first plurality of layers including at least a first convolutional layer associated with at least a first filter comprising a first set of filter weights and (ii) a scorer;
(D) obtaining a plurality of scores from the scorer, wherein each score in the plurality of scores corresponds to the input of a vector set in the plurality of vector sets into the network architecture;
and
(E) using a comparison of respective scores in the plurality of scores to the corresponding cancer condition of the corresponding training subject in the plurality of training subjects to adjust at least the first set of filter weights thereby training the network architecture to classify a cancer condition, in the plurality of cancer conditions.

7 more elements

A computer system for classifying a cancer condition, in a plurality of different cancer conditions, for a subject, the computer system comprising:

at least one processor;
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:
(A) obtaining a genotypic data construct for the subject that includes genotypic information corresponding to locations of a reference genome of the species;
(B) formatting the genotypic data construct into a corresponding vector set comprising one or more corresponding vectors;
(C) providing the vector set to a network architecture that includes at least (i) a first convolutional neural network path comprising a first plurality of layers including at least a first convolutional layer associated with at least a first filter comprising a first set of filter weights and (ii) a scorer;
(D) obtaining a first score from the scorer, wherein the first score corresponds to the input of the vector set into the network architecture;
and
(E) using the first score to classify the subject as having a cancer condition, in the plurality of cancer conditions.

6 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/428,575Filed 31 May 2019
Priority: 1 Jun 2018
Published: 2 Jan 2020
Term ends: Not estimatedThe source records don’t support an estimate.

Classification20 codes

G16B 30/10
ICT specially adapted for sequence analysis involving nucleotides or amino acids: sequence alignment; Homology search
G06N 3/04
Neural networks: architecture, e.g. interconnection topology
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks
G06N 3/0464
Architecture, e.g. interconnection topology: convolutional networks [CNN, ConvNet]

Family

Application62/679,746Us provisional application · filed 1 Jun 2018
US 11,482,303 B2Related patent
US 2020/0005899 A1This patent

From the US filing history. Foreign family members aren’t listed.

Timeline

Citations

Open elsewhere

Building an agent or pulling many patents? Use the API or MCP server instead of scraping pages. Get an API key