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Steven J. Rysavy

Researcher at University of Washington

Publications -  12
Citations -  562

Steven J. Rysavy is an academic researcher from University of Washington. The author has contributed to research in topics: Computer-aided diagnosis & Medical diagnosis. The author has an hindex of 8, co-authored 12 publications receiving 463 citations. Previous affiliations of Steven J. Rysavy include San Francisco State University.

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Distance restraints from crosslinking mass spectrometry: Mining a molecular dynamics simulation database to evaluate lysine–lysine distances

TL;DR: The Dynameomics database is used, a repository of high‐quality molecular dynamics simulations of 807 proteins representative of diverse protein folds, to investigate the relationship between lysine–lysine distances in experimental starting structures and in simulation ensembles and concludes that for DSS/BS3, a distance constraint of 26–30 Å between Cα atoms is appropriate.
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Dynameomics: A Comprehensive Database of Protein Dynamics

TL;DR: This work has performed molecular dynamics simulations of the native state and unfolding pathways of over 2000 protein/peptide systems representing the majority of folds in globular proteins, stored and organized using an innovative database approach.
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New Dynamic Rotamer Libraries: Data-Driven Analysis of Side-Chain Conformational Propensities

TL;DR: A backbone-dependent rotamer library, based on secondary structure ϕ/ψ regions, and an update to the 2011 backbone-independent library that addresses the doubling of the authors' dataset since its original publication are provided.
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Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans.

TL;DR: Experimental results of the authors show that the proposed CAD system behaves in clearer agreement with the CBCT ground-truth than with histopathology, supporting the Simon's conjecture that CBCT diagnosis can be as accurate as Histopathology for differentiating the periapical lesions.
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DIVE: A Graph-Based Visual-Analytics Framework for Big Data

TL;DR: DIVE is a data-agnostic, ontologically expressive software framework that can stream large datasets at interactive speeds that makes novel contributions to structured-data-model manipulation and high-throughput streaming of large, structured datasets.