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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Veröffentlicht in:	IEEE/ACM transactions on computational biology and bioinformatics Jg. 17; H. 2; S. 536 - 546
Hauptverfasser:	Li, Yujie, Huang, Heng, Chen, Hanbo, Liu, Tianming
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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