Scientific Results

These papers and posters used Paradigm4’s REVEAL™ / SciDB translational analytics

software platform to manage their data and compute their results.

The V122I Mutation in Hereditary Transthyretin-Mediated Amyloidosis Is Significantly Associated with Polyneuropathy

Margaret M. Parker , Scott M. Damrauer , Daniel J. Rader , Simina Ticau , David Erbe , Gregory Hinkle , and Paul Nioi
HFSA 23rd Annual Scientific Meeting (September 2019)

Transthyretin-stabilizing mutation T119M is not associated with protection against vascular disease or death in the UK Biobank

Margaret M. Parker, Simina Ticau, James Butler, David Erbe, Madeline Merkel, Emre Aldinc, Gregory Hinkle, Paul Nioi
bioRxiv Sept 2019

Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology

Yosuke Tanigawa, Jiehan Li, Johanne M. Justesen, Heiko Horn, Matthew Aguirre, Christopher DeBoever, Chris Chang, Balasubramanian Narasimhan, Kasper Lage, Trevor Hastie, Chong Y. Park, Gill Bejerano, Erik Ingelsson & Manuel A. Rivas
Nature Communication 2019

Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study

Christopher DeBoever, Yosuke Tanigawa, Malene E. Lindholm, Greg McInnes, Adam Lavertu,,Erik Ingelsson, Chris Chang, Euan A. Ashley, Carlos D. Bustamante, Mark J. Daly, Manuel A. Rivas
Nature Communications 2018

Bayesian model comparison for rare variant association studies of multiple phenotypes

Christopher DeBoever, Matthew Aguirre, Yosuke Tanigawa, Chris C. A. Spencer, Timothy Poterba, Carlos D. Bustamante, Mark J. Daly, Matti Pirinen, Manuel A. Rivas
bioRxiv 2018

A Big Data Platform for Surface Enhanced Raman Spectroscopy Data with An Application on Image-based Sensor Quality Control

Yiming Zuo, Rares Vernica, Yang Lei, Steven Barcelo, Anita Rogacs
2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), San Jose, CA, USA, 2019, pp. 463-466

Visual Analytics with Unparalleled Variety Scaling for Big Earth Data

Lina Yu , Michael L. Rilee , Yu Pan , Feiyu Zhu , Kwo-Sen Kuo , Hongfeng Yu
2017 IEEE Conference on Big Data