US 11,636,920 B2Granted

Systems and methods for generating and training convolutional neural networks using biological sequences and relevance scores derived from structural, biochemical, population and evolutionary data

AssigneeDeep Genomics Incorporated

InventorsHui Yuan Xiong, Brendan Frey

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Abstract

We describe systems and methods for generating and training convolutional neural networks using biological sequences and relevance scores derived from structural, biochemical, population and evolutionary data. The convolutional neural networks take as input biological sequences and additional information and output molecular phenotypes. Biological sequences may include DNA, RNA and protein sequences. Molecular phenotypes may include protein-DNA interactions, protein-RNA interactions, protein-protein interactions, splicing patterns, polyadenylation patterns, and microRNA-RNA interactions, which may be described using numerical, categorical or ordinal attributes. Intermediate layers of the convolutional neural networks are weighted using relevance score sequences, for example, conservation tracks. The resulting molecular phenotype convolutional neural networks may be used in genetic testing, to identify drug targets, to identify patients that respond similarly to a drug, to ascertain health risks, or to connect patients that have similar molecular phenotypes.

Claims32

Dependent claims

A system for weighting convolutional layers in a molecular phenotype convolutional neural network (MPCNN), the system comprising:

a. the MPCNN comprising at least three layers, each of the at least three layers configured to receive inputs and produce outputs, a first layer of the at least three layers configured to obtain a biological sequence comprising a plurality of positions, a last layer of the at least three layers representing a molecular phenotype, each layer of the at least three layers other than the first layer configured to receive inputs from the produced outputs of one or more prior layers of the at least three layers;
b. one or more of the at least three layers configured as convolutional layers, each of the convolutional layers comprising one or more convolutional filters linking the received inputs of the convolutional layer to produced outputs of the convolutional layer, the received inputs of the convolutional layer comprising a plurality of convolutional layer input positions, the produced outputs of the convolutional layer comprising a plurality of convolutional layer output positions;
and
c. one or more weighting units, each of the one or more weighting units linked to at least one of the one or more convolutional filters of a convolutional layer, each of the one or more weighting units associated with a relevance score sequence, each of the relevance score sequences comprising a plurality of relevance score sequence positions, each of the plurality of relevance score sequence positions associated with a numerical value, wherein the numerical value quantifies a biological relevance of a corresponding position in the biological sequence with respect to the at least one of the one or more convolutional filters of a convolutional layer, each of the one or more weighting units configured to use the associated relevance score sequence to weight operations of the associated convolutional filter of the one or more convolutional filters.

1 more element

A method for weighting layers in a molecular phenotype convolutional neural network (MPCNN), the method comprising:

a. obtaining the MPCNN comprising at least three layers, each of the at least three layers receiving inputs and producing outputs, a first layer of the at least three layers obtaining a biological sequence comprising a plurality of positions, a last layer of the at least three layers representing a molecular phenotype, each layer of the at least three layers other than the first layer receiving inputs from the produced outputs of one or more prior layers of the at least three layers, wherein one or more of the at least three layers are convolutional layers, each of the convolutional layers comprising one or more convolutional filters linking the received inputs of the convolutional layer to produced outputs of the convolutional layer, the received inputs of the convolutional layer comprising a plurality of convolutional layer input positions, the produced outputs of the convolutional layer comprising a plurality of convolutional layer output positions;
b. obtaining one or more relevance score sequences, each of the one or more relevance score sequences comprising a plurality of relevance score sequence positions, each of the plurality of relevance score sequence positions associated with a numerical value, wherein the numerical value quantifies a biological relevance of a corresponding position in the biological sequence with resect to the at least one of the one or more convolutional filters of a convolutional layer;
and
c. applying one or more weighting operations, wherein each weighting operation of the one or more weighting operations comprises using an associated relevance score sequence in the one or more relevance score sequences to weight operations of an associated convolutional filter of the one or more convolutional filters.

1 more element

Dependencies are inferred from the claim text.

Facts

Application: 16/230,149Filed 21 Dec 2018 · continuation
Priority: 4 Jul 2016
Published: US 2019/0220740 A118 Jul 2019
Granted: 25 Apr 2023
Term ends: Not estimatedThe source records don’t support an estimate.

Classification25 codes

G16B 20/20
ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations: allele or variant detection, e.g. single…
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks
G06N 3/0455
Combinations of networks: auto-encoder networks; Encoder-decoder networks
G06N 3/0464
Architecture, e.g. interconnection topology: convolutional networks [CNN, ConvNet]