Deep Neural Networks for In Situ Hybridization Grid Completion and Clustering

Transcriptome in brain plays a crucial role in understanding the cortical organization and the development of brain structure and function. Two challenges, incomplete data and high dimensionality of transcriptome, remain unsolved. Here, we present a novel training scheme that successfully adapts the...

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Vydané v:	IEEE/ACM transactions on computational biology and bioinformatics Ročník 17; číslo 2; s. 536 - 546
Hlavní autori:	Li, Yujie, Huang, Heng, Chen, Hanbo, Liu, Tianming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.03.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Animals Architecture Artificial neural networks Atlases as Topic Belief networks Bioinformatics Brain Brain - diagnostic imaging Brain - metabolism Cerebral cortex Cluster Analysis Clustering Computational Biology - methods Computer architecture Deep belief network fully convolutional neural network Functional anatomy Gene expression Gene Expression Profiling - methods Hybridization Image Processing, Computer-Assisted - methods In Situ Hybridization Machine learning Measurement Mice Neural networks Neural Networks, Computer Noise reduction Organizations restricted Boltzmann machines Structure-function relationships Training Transcriptome transcriptome architecture
ISSN:	1545-5963, 1557-9964, 1557-9964
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Shrnutí:	Transcriptome in brain plays a crucial role in understanding the cortical organization and the development of brain structure and function. Two challenges, incomplete data and 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 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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1545-5963 1557-9964 1557-9964
DOI:	10.1109/TCBB.2018.2864262