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Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations

Junghoon Chae, Debsindhu Bhowmik, Heng Ma, Arvind Ramanathan, Chad Steed
doi: https://doi.org/10.1101/830844
Junghoon Chae
*Oak Ridge National Laboratory, Oak Ridge, USA
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Debsindhu Bhowmik
*Oak Ridge National Laboratory, Oak Ridge, USA
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  • For correspondence: bhowmikd@ornl.gov
Heng Ma
†Argonne National Laboratory, Lemont, USA
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Arvind Ramanathan
†Argonne National Laboratory, Lemont, USA
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Chad Steed
*Oak Ridge National Laboratory, Oak Ridge, USA
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Abstract

Molecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).

Footnotes

  • chaej{at}ornl.gov, steedca{at}ornl.gov, heng.ma{at}anl.gov, ramanathana{at}anl.gov

  • Notice: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted November 04, 2019.
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Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations
Junghoon Chae, Debsindhu Bhowmik, Heng Ma, Arvind Ramanathan, Chad Steed
bioRxiv 830844; doi: https://doi.org/10.1101/830844
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Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations
Junghoon Chae, Debsindhu Bhowmik, Heng Ma, Arvind Ramanathan, Chad Steed
bioRxiv 830844; doi: https://doi.org/10.1101/830844

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