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

Pacific Symposium on Biocomputing

Volumn 0, Issue , 2018, Pages 80-95

  Way, Gregory P ^a   Greene, Casey S ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Deep learning; Gene expression; The cancer genome atlas; Variational autoencoder

Indexed keywords

DEEP NEURAL NETWORKS; DISEASES; GENE THERAPY; GENOME; TUMORS;

AUTO ENCODERS; CANCER GENOME; DEEP LEARNING; EXPRESSION MEASUREMENTS; GENE EXPRESSION LEVELS; GENES EXPRESSION; GENOMICS; THE CANCER GENOME ATLAS; TRANSCRIPTOMES; VARIATIONAL AUTOENCODER;

GENE EXPRESSION;

TRANSCRIPTOME;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; BIOLOGY; BOOK; CLASSIFICATION; FEMALE; GENE EXPRESSION PROFILING; GENETICS; HUMAN; MALE; NEOPLASM; NONLINEAR SYSTEM; OVARY TUMOR; PROCEDURES; STATISTICS AND NUMERICAL DATA; SYSTEMS BIOLOGY; UNSUPERVISED MACHINE LEARNING;

ALGORITHMS; ATLASES AS TOPIC; COMPUTATIONAL BIOLOGY; FEMALE; GENE EXPRESSION PROFILING; HUMANS; MALE; MODELS, GENETIC; NEOPLASMS; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; OVARIAN NEOPLASMS; SYSTEMS BIOLOGY; TRANSCRIPTOME; UNSUPERVISED MACHINE LEARNING;

EID: 85048499332     PISSN: 23356928     EISSN: 23356936     Source Type: Journal    
DOI: 10.1142/9789813235533_0008     Document Type: Conference Paper

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