Indexed on: 15 Aug '18Published on: 15 Aug '18Published in: IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM
Transcriptome in brain plays a crucial role in understanding the cortical organization and the development of brain structure and function. Two challenges, the incomplete data and the high dimensionality of transcriptome, remain unsolved. Here we present a novel training scheme that successfully adapts the U-net architecture to the problem of volume recovery. By analogy to denoising autoencoder, we hide a portion of each training sample so that the network can learn to recover missing voxels from the context. Then on the completed volumes, we show that restricted Boltzmann Machines (RBMs) can be used to infer co-occurrences among voxels, providing foundations for dividing the cortex into discrete subregions. As we stack multiple RBMs to form a deep belief network (DBN), we progressively map the high-dimensional raw input into abstract representations and create a hierarchy of transcriptome architecture. A coarse to fine organization emerges from the network layers. This organization incidentally corresponds to the anatomical structures, suggesting a close link between structures and the genetic underpinnings. Thus, we demonstrate a new way of learning transcriptome-based hierarchical organization using RBM and DBN.