Evidence map›Paper›PMID 35491772›Full record

ArticleCancer biomarkers : section A of Disease markers2022

From space to biomedicine: Enabling biomarker data science in the cloud.

D J Crichton, L Cinquini, H Kincaid, A Mahabal, A Altinok, K Anton, M Colbert, S Kelly, D Liu, C Patriotis and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer biomarkers : section A of Disease markers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

D J CrichtonJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
L CinquiniJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
H KincaidJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
A MahabalCalifornia Institute of Technology, Pasadena, CA, USA.
A AltinokJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
K AntonUniversity of North Carolina, Chapel Hill, NC, USA.
M ColbertUniversity of North Carolina, Chapel Hill, NC, USA.
S KellyJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
D LiuJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
C Patriotis, Bethesda, MD, USA.
S LombeydaCalifornia Institute of Technology, Pasadena, CA, USA.
S Srivastava, Bethesda, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

NASA's Jet Propulsion Laboratory (JPL) is advancing research capabilities for data science with two of the National Cancer Institute's major research programs, the Early Detection Research Network (EDRN) and the Molecular and Cellular Characterization of Screen-Detected Lesions (MCL), by enabling data-driven discovery for cancer biomarker research. The research team pioneered a national data science ecosystem for cancer biomarker research to capture, process, manage, share, and analyze data across multiple research centers. By collaborating on software and data-driven methods developed for space and earth science research, the biomarker research community is heavily leveraging similar capabilities to support the data and computational demands to analyze research data. This includes linking diverse data from clinical phenotypes to imaging to genomics. The data science infrastructure captures and links data from over 1600 annotations of cancer biomarkers to terabytes of analysis results on the cloud in a biomarker data commons known as "LabCAS". As the data increases in size, it is critical that automated approaches be developed to "plug" laboratories and instruments into a data science infrastructure to systematically capture and analyze data directly. This includes the application of artificial intelligence and machine learning to automate annotation and scale science analysis.

Indexed as

Artificial IntelligenceData ScienceBiomarkers, TumorEcosystemHumansSoftwareBiomarkers, Tumorartificial intelligencebig datacloud computingdata analysisData sciencemachine learning

Identifiers

PMID35491772
PMCPMC12364163

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.