Abstract: Systems and methods for variant calling in single molecule sequencing from a genomic dataset using a convolutional deep neural network. The method includes: transforming properties of each of the variants into a multi-dimensional tensor; passing the multi-dimensional tensors through a trained convolutional deep neural network to predict categorical output variables, the convolutional deep neural network minimizing a cost function iterated over each variant, the convolutional deep neural network trained using a training genomic dataset including previously identified variants, the convolutional neural network including: a plurality of pooled convolutional layers and at least two fully-connected layers connected sequentially after the last of the pooled convolutional layers, the at least two fully-connected layers comprising a second fully-connected layer connected sequentially after a first fully-connected layer; and outputting the predicted categorical output variables.

Abstract: Systems and methods for variant calling in single molecule sequencing from a genomic dataset using a convolutional deep neural network. The method includes: transforming properties of each of the variants into a multi-dimensional tensor; passing the multi-dimensional tensors through a trained convolutional deep neural network to predict categorical output variables, the convolutional deep neural network minimizing a cost function iterated over each variant, the convolutional deep neural network trained using a training genomic dataset including previously identified variants, the convolutional neural network including: a plurality of pooled convolutional layers and at least two fully-connected layers connected sequentially after the last of the pooled convolutional layers, the at least two fully-connected layers comprising a second fully-connected layer connected sequentially after a first fully-connected layer; and outputting the predicted categorical output variables.