Statistical Inference for Big Data Problems in Molecular Biophysics

Conference · Sun Jan 01 00:00:00 EST 2012

OSTI ID:1055187

Ramanathan, Arvind ^[1]; Savol, Andrej ^[2]; Burger, Virginia ^[2]; Quinn, Shannon ^[2]; Agarwal, Pratul K ^[1]; Chennubhotla, Chakra ^[2]

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1. ORNL
2. University of Pittsburgh School of Medicine, Pittsburgh PA

We highlight the role of statistical inference techniques in providing biological insights from analyzing long time-scale molecular simulation data. Technologi- cal and algorithmic improvements in computation have brought molecular simu- lations to the forefront of techniques applied to investigating the basis of living systems. While these longer simulations, increasingly complex reaching petabyte scales presently, promise a detailed view into microscopic behavior, teasing out the important information has now become a true challenge on its own. Mining this data for important patterns is critical to automating therapeutic intervention discovery, improving protein design, and fundamentally understanding the mech- anistic basis of cellular homeostasis.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Center for Computational Sciences (NCCS)
• Sponsoring Organization: USDOE
• DOE Contract Number: DE-AC05-00OR22725
• OSTI ID: 1055187
• Resource Relation: Conference: Neural Information Processing Systems: Workshop on Big Learning, South Lake Tahoe, CA, USA, 20121207, 20121208
• Country of Publication: United States
• Language: English