Cellular State Transformations Using Deep Learning for Precision Medicine Applications

Cellular State Transformations Using Deep Learning for Precision Medicine Applications

Summary: We introduce the Transcriptome State Perturbation Generator (TSPG) as a novel deep-learning method to identify changes in genomic expression that occur between tissue states using generative adversarial networks. TSPG learns the transcriptome perturbations from RNA-sequencing data required...

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Bibliographic Details
Main Authors: Colin Targonski, M. Reed Bender, Benjamin T. Shealy, Benafsh Husain, Bill Paseman, Melissa C. Smith, F. Alex Feltus
Format: Article
Language:English
Published: Elsevier 2020-09-01
Series:Patterns
Subjects:
deep learning
generative adversarial networks
tumor biology
gene expression
precision medicine
renal cell carcinoma
Online Access:http://www.sciencedirect.com/science/article/pii/S266638992030115X

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http://www.sciencedirect.com/science/article/pii/S266638992030115X

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