Deep learning-based techniques for training deep convolutional neural networks

A non-transitory computer readable storage medium impressed with computer program instructions to construct a convolutional neural network-based classifier for variant classification, the instructions, when executed on a processor, implement a method comprising:

• training a convolutional neural network-based classifier, which runs on numerous processors coupled to memory, on training data using a backpropagation-based gradient update technique that progressively matches outputs of the convolutional neural network-based classifier with corresponding ground truth labels;
• wherein the convolutional neural network-based classifier comprises groups of residual blocks;
• wherein 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;
• wherein the convolution window size varies between the groups of residual blocks;
• wherein the atrous convolution rate varies between the groups of residual blocks;
• wherein the training data includes pairs of translated sequences generated from benign variants and pathogenic variants and used as benign training examples and as pathogenic training examples;
• and
• wherein the benign variants include common human missense variants and non-human primate missense variants occurring on alternative non-human primate base triplet sequences that share matching reference base triplet sequences with humans.

A system including one or more processors coupled to memory, the memory loaded with computer instructions to construct a convolutional neural network-based classifier for variant classification, the instructions, when executed on the processors, implement actions comprising:

• training a convolutional neural network-based classifier, which runs on numerous processors coupled to memory, on training data using a backpropagation-based gradient update technique that progressively matches outputs of the convolutional neural network-based classifier with corresponding ground truth labels;
• wherein the convolutional neural network-based classifier comprises groups of residual blocks;
• wherein 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;
• wherein the convolution window size varies between the groups of residual blocks;
• wherein the atrous convolution rate varies between the groups of residual blocks;
• wherein the training data includes pairs of translated sequences generated from benign variants and pathogenic variants and used as benign training examples and as pathogenic training examples;
• and
• wherein the benign variants include common human missense variants and non-human primate missense variants occurring on alternative non-human primate base triplet sequences that share matching reference base triplet sequences with humans.