Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders

The Cancer Genome Atlas (TCGA) has profiled over 10,000 tumors across 33 different cancer-types for many genomic features, including gene expression levels. Gene expression measurements capture substantial information about the state of each tumor. Certain classes of deep neural network models are c...

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Veröffentlicht in:	Biocomputing 2018 Jg. 23; S. 80 - 91
Hauptverfasser:	Way, Gregory P., Greene, Casey S.
Format:	Buchkapitel Journal Article
Sprache:	Englisch
Veröffentlicht:	United States WORLD SCIENTIFIC 01.01.2018
Schlagworte:	Algorithms APPLICATIONS OF GENETICS, GENOMICS AND BIOINFORMATICS IN DRUG DISCOVERY Atlases as Topic Computational Biology - methods Female Gene Expression Profiling - statistics & numerical data Humans Male Models, Genetic Neoplasms - genetics Neural Networks, Computer Nonlinear Dynamics Ovarian Neoplasms - classification Ovarian Neoplasms - genetics Systems Biology Transcriptome Unsupervised Machine Learning Gene Expression Variational Autoencoder, The Cancer Genome Atlas Deep Learning
ISBN:	9789813235526, 9789813235540, 9813235543, 9813235527, 9789813235533, 9813235535
ISSN:	2335-6936, 2335-6936
Online-Zugang:	Volltext
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