Skip to main content
Advertisement
DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes
Fig 1
(a) Data inputs includes Hi-C matrix, accessible chromatin landscapes, and enriched experimental data such as ChIA-PET/HiChIP and Capture Hi-C as positive interactions. (b) Positive samples are generated according to the input data, and negative samples are generated according to the similar distance or greater distance of the positive samples. (c) DLoopCaller includes three convolutional blocks, two fully connected layers and a classification layer, in which each block consists of a convolutional layer, a ReLU layer, a dropout layer, and followed by a global average pooling layer.