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Exploring the Effects of Spaceflight on Mouse Physiology using the Open Access NASA GeneLab Platform

Exploring the Effects of Spaceflight on Mouse Physiology using the Open Access NASA GeneLab Platform

Published on: January 13, 2019

OPEN DATA FOR DISCOVERY SCIENCE.

Philip R O Payne¹, Kun Huang, Nigam H Shah

¹Washington University Institute for Informatics, Washington University in St. Louis School of Medicine, St. Louis, MO 63130, United States of America, prpayne@wustl.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|November 30, 2016

Summary

This summary is machine-generated.

Open data sharing and analysis are revolutionizing healthcare and life sciences discovery. This approach enables new insights for precision medicine, drug repurposing, and population health research.

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Area of Science:

Life Sciences
Healthcare
Biomedical Research

Background:

The healthcare and life sciences sector is increasingly adopting open, data-centric models for scientific discovery.
This shift necessitates robust methods for sharing, discovering, reusing, integrating, and analyzing diverse open biological, clinical, and population data.

Framework:

Addresses the challenges and opportunities in leveraging open data for scientific advancement.
Focuses on methodological best practices for data integration and analysis in discovery science.

Implementation:

Explores the use of open data for hypothesis generation and testing.
Discusses both primary use (open data as the core) and secondary use (augmenting proprietary data) of open datasets.

Implications:

Enables identification of novel biomarkers for precision medicine.
Facilitates drug repurposing for cost-effective therapies.
Improves understanding of population-level factors influencing health and disease.