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...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE/ACM transactions on computational biology and bioinformatics Vol. 17; no. 2; pp. 536 - 546
Main Authors:	Li, Yujie, Huang, Heng, Chen, Hanbo, Liu, Tianming
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.03.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!