CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12-21

AUTHORS

Justin M. Wozniak, Rajeev Jain, Prasanna Balaprakash, Jonathan Ozik, Nicholson T. Collier, John Bauer, Fangfang Xia, Thomas Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina Garcia Cardona, Brian Van Essen, Matthew Baughman

ABSTRACT

BACKGROUND: Current multi-petaflop supercomputers are powerful systems, but present challenges when faced with problems requiring large machine learning workflows. Complex algorithms running at system scale, often with different patterns that require disparate software packages and complex data flows cause difficulties in assembling and managing large experiments on these machines. RESULTS: This paper presents a workflow system that makes progress on scaling machine learning ensembles, specifically in this first release, ensembles of deep neural networks that address problems in cancer research across the atomistic, molecular and population scales. The initial release of the application framework that we call CANDLE/Supervisor addresses the problem of hyper-parameter exploration of deep neural networks. CONCLUSIONS: Initial results demonstrating CANDLE on DOE systems at ORNL, ANL and NERSC (Titan, Theta and Cori, respectively) demonstrate both scaling and multi-platform execution. More... »

PAGES

491

References to SciGraph publications

  • 2012. Parallel Algorithm Configuration in LEARNING AND INTELLIGENT OPTIMIZATION
  • 2014-06-01. A community effort to assess and improve drug sensitivity prediction algorithms in NATURE BIOTECHNOLOGY
  • 2015-05-27. Deep learning in NATURE
  • 2001-10. Random Forests in MACHINE LEARNING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s12859-018-2508-4

    DOI

    http://dx.doi.org/10.1186/s12859-018-2508-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1110822344

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30577736


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