Abstract
With biomedical researchers analyzing stars and astronomers tackling cancer, two unlikely collaborations creatively solve data problems.
Keywords
AstronomerStarsData scienceArt historyComputer scienceHistoryAstronomyPhysics
MeSH Terms
AlgorithmsAstronomyBiomarkersTumorHumansImagingThree-DimensionalInterdisciplinary CommunicationSoftware
Related Publications
Statistics, Data Mining, and Machine Learning in Astronomy
As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing ...
Opportunities and obstacles for deep learning in biology and medicine
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently show...
Immunology of Gynecologic Neoplasms: Analysis of the Prognostic Significance of the Immune Status
Gynecologic Oncology has changed in the last few decades. An increasing proportion of patients is benefiting from long term survival although patients diagnosed with advanced di...
The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update
Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists acros...
Surface-Enhanced Raman Spectroscopy for Bioanalysis: Reliability and Challenges
Surface-enhanced Raman spectroscopy (SERS) inherits the rich chemical fingerprint information on Raman spectroscopy and gains sensitivity by plasmon-enhanced excitation and scat...
Publication Info
- Year
- 2011
- Type
- article
- Volume
- 331
- Issue
- 6018
- Pages
- 696-697
- Citations
- 9
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
9
OpenAlex
0
Influential
8
CrossRef
Cite This
Sarah Reed
(2011).
Is There an Astronomer in the House?.
Science
, 331
(6018)
, 696-697.
https://doi.org/10.1126/science.331.6018.696
Identifiers
- DOI
- 10.1126/science.331.6018.696
- PMID
- 21311004