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

author

Casey S. Greene

published in: Pacific Symposium on Biocomputing

date of publication: 2018-01-01

language: English

main subject: variational auto-encoder

Cites articles 18

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2017-07-12

Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks

Laub MT, Casey S. Greene
2017-07-13

druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

Artur Kadurin, Sergey Nikolenko, Alex Zhavoronkov, Alex Aliper
2017-01-25

Dermatologist-level classification of skin cancer with deep neural networks

Sebastian Thrun, Helen Blau
2017-02-01

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

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2017-07-01

WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit

Suhas Vasaikar
2016-01-19

ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions

Casey S. Greene
2016-10-11

Comprehensive Cross-Population Analysis of High-Grade Serous Ovarian Cancer Supports No More Than Three Subtypes

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Predicting effects of noncoding variants with deep learning-based sequence model

Olga Troyanskaya
2015-01-01

Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders

Casey S. Greene
2014-09-30

Prognostic and therapeutic relevance of molecular subtypes in high-grade serous ovarian cancer

Charles Ginther, Judy Dering, Boris Winterhoff, Ellen L Goode, Chen Wang, Lynn C. Hartmann, Kimberly R Kalli, Gottfried E Konecny, Sean C Dowdy, William A Cliby, Bobbie S Gostout, Gary L Keeney, Dennis Slamon
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Article - wd:Q47132893

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