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Casey S Greene

Showing results (21-30 of 185) with videos related to

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Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|December 9, 2017
Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencodersGregory P Way, Casey S Greene
Annals of the New York Academy of Sciences|January 25, 2012
Accurate evaluation and analysis of functional genomics data and methodsCasey S Greene, Olga G Troyanskaya
Nucleic Acids Research|June 10, 2011
PILGRM: an interactive data-driven discovery platform for expert biologistsCasey S Greene, Olga G Troyanskaya
Nature Methods|December 4, 2018
Bayesian deep learning for single-cell analysisGregory P Way, Casey S Greene
Seminars in Nephrology|November 4, 2010
Integrative systems biology for data-driven knowledge discoveryCasey S Greene, Olga G Troyanskaya
Computational and Structural Biotechnology Journal|July 9, 2020
Constructing knowledge graphs and their biomedical applicationsDavid N Nicholson, Casey S Greene
Journal of Biomedical Informatics|October 17, 2016
Semi-supervised learning of the electronic health record for phenotype stratificationBrett K Beaulieu-Jones, Casey S Greene,
Bioinformatics Advances|January 29, 2024
Optimizer's dilemma: optimization strongly influences model selection in transcriptomic predictionJake Crawford, Maria Chikina, Casey S Greene
Nature Biotechnology|March 14, 2017
Reproducibility of computational workflows is automated using continuous analysisBrett K Beaulieu-Jones, Casey S Greene
Patterns (New York, N.Y.)|January 8, 2025
Best holdout assessment is sufficient for cancer transcriptomic model selectionJake Crawford, Maria Chikina, Casey S Greene
Pageof 19

Showing results (21-30 of 185) with videos related to

Sort By:
Pageof 19
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|December 9, 2017
Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencodersGregory P Way, Casey S Greene
Annals of the New York Academy of Sciences|January 25, 2012
Accurate evaluation and analysis of functional genomics data and methodsCasey S Greene, Olga G Troyanskaya
Nucleic Acids Research|June 10, 2011
PILGRM: an interactive data-driven discovery platform for expert biologistsCasey S Greene, Olga G Troyanskaya
Nature Methods|December 4, 2018
Bayesian deep learning for single-cell analysisGregory P Way, Casey S Greene
Seminars in Nephrology|November 4, 2010
Integrative systems biology for data-driven knowledge discoveryCasey S Greene, Olga G Troyanskaya
Computational and Structural Biotechnology Journal|July 9, 2020
Constructing knowledge graphs and their biomedical applicationsDavid N Nicholson, Casey S Greene
Journal of Biomedical Informatics|October 17, 2016
Semi-supervised learning of the electronic health record for phenotype stratificationBrett K Beaulieu-Jones, Casey S Greene,
Bioinformatics Advances|January 29, 2024
Optimizer's dilemma: optimization strongly influences model selection in transcriptomic predictionJake Crawford, Maria Chikina, Casey S Greene
Nature Biotechnology|March 14, 2017
Reproducibility of computational workflows is automated using continuous analysisBrett K Beaulieu-Jones, Casey S Greene
Patterns (New York, N.Y.)|January 8, 2025
Best holdout assessment is sufficient for cancer transcriptomic model selectionJake Crawford, Maria Chikina, Casey S Greene
Pageof 19