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Title: Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations

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).

Authors:
ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [2]; ORCiD logo [1]
  1. ORNL
  2. Argonne National Laboratory (ANL)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1606818
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: IEEE International Conference on Big Data - Los Angeles, California, United States of America - 12/9/2019 5:00:00 AM-12/12/2019 5:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Chae, Junghoon, Bhowmik, Debsindhu, Ma, Heng, Ramanathan, Arvind, and Steed, Chad. Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations. United States: N. p., 2019. Web. doi:10.1109/BigData47090.2019.9006048.
Chae, Junghoon, Bhowmik, Debsindhu, Ma, Heng, Ramanathan, Arvind, & Steed, Chad. Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations. United States. https://doi.org/10.1109/BigData47090.2019.9006048
Chae, Junghoon, Bhowmik, Debsindhu, Ma, Heng, Ramanathan, Arvind, and Steed, Chad. Sun . "Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations". United States. https://doi.org/10.1109/BigData47090.2019.9006048. https://www.osti.gov/servlets/purl/1606818.
@article{osti_1606818,
title = {Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations},
author = {Chae, Junghoon and Bhowmik, Debsindhu and Ma, Heng and Ramanathan, Arvind and Steed, Chad},
abstractNote = {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).},
doi = {10.1109/BigData47090.2019.9006048},
url = {https://www.osti.gov/biblio/1606818}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {12}
}

Conference:
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