A method for predicting DNA accessibility in a genomic sample, comprising: obtaining, by a training engine, genomic sample data including DNase-seq data files and RNA-seq data files for a plurality of cell types; generating, by the training engine, paired data files from the genomic sample data by assigning DNase-seq data files to RNA-seq data files that are at least within a same biotype; configuring a neural network to be trained to predict DNA accessibility based on RNA-seq data using a plurality of batches of the paired data files, wherein configuring the neural network comprises configuring: convolutional layers of the neural network to process a first input comprising DNA sequence data from one of the paired data files to generate a convolved output, and fully connected layers of the neural network following the convolutional layers to concatenate the convolved output with a second input comprising gene expression levels derived from RNA-seq data from the one of the paired data files and process the concatenation to generate a DNA accessibility prediction output; training the neural network using the plurality of batches of the paired data files; and configuring a computing device to use the trained neural network to predict DNA accessibility in a genomic sample input comprising RNA-seq data and whole genome sequencing for a new cell type with respect to the genomic sample data. obtaining, by a training engine, genomic sample data including DNase-seq data files and RNA-seq data files for a plurality of cell types; generating, by the training engine, paired data files from the genomic sample data by assigning DNase-seq data files to RNA-seq data files that are at least within a same biotype; configuring a neural network to be trained to predict DNA accessibility based on RNA-seq data using a plurality of batches of the paired data files, wherein configuring the neural network comprises configuring: convolutional layers of the neural network to process a first input comprising DNA sequence data from one of the paired data files to generate a convolved output, and fully connected layers of the neural network following the convolutional layers to concatenate the convolved output with a second input comprising gene expression levels derived from RNA-seq data from the one of the paired data files and process the concatenation to generate a DNA accessibility prediction output; convolutional layers of the neural network to process a first input comprising DNA sequence data from one of the paired data files to generate a convolved output, and fully connected layers of the neural network following the convolutional layers to concatenate the convolved output with a second input comprising gene expression levels derived from RNA-seq data from the one of the paired data files and process the concatenation to generate a DNA accessibility prediction output; training the neural network using the plurality of batches of the paired data files; and configuring a computing device to use the trained neural network to predict DNA accessibility in a genomic sample input comprising RNA-seq data and whole genome sequencing for a new cell type with respect to the genomic sample data.