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AssigneeIllumina, Inc.
InventorsKishore JAGANATHAN, Kai-How FARH, Sofia KYRIAZOPOULOU PANAGIOTOPOULOU, Jeremy Francis McRAE
Estimating the term…
The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional network network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.
A neural network-implemented method of training a convolutional neural network, the method including:
A system including one or more processors coupled to memory, the memory loaded with computer instructions to train a convolutional neural network, the instructions, when executed on the processors, implement actions comprising:
A non-transitory computer readable storage medium impressed with computer program instructions to train a convolutional neural network, the instructions, when executed on a processor, implement a method comprising:
Dependencies are inferred from the claim text.