Evidence map›Paper›PMID 28983483›Full record

ArticleFrontiers in cell and developmental biology2017

A Comprehensive Infrastructure for Big Data in Cancer Research: Accelerating Cancer Research and Precision Medicine.

Izumi V Hinkson, Tanja M Davidsen, Juli D Klemm, Anthony R Kerlavage, Warren A Kibbe, Ishwar Chandramouliswaran

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Frontiers in cell and developmental biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 54 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 6% of its field
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

54 citing papers in PubMed, 1 synthesis or guideline pooled it, 87 citations in OpenAlex.

  1. Pooled it
  2. The Common Fund Data Ecosystem (CFDE).bioRxiv : the preprint server for biology · 2026
    Article
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  4. Article
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  7. Article
  8. Review
  9. Article
  10. Review
  11. The Use of Next-Generation Sequencing in Personalized Medicine.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Izumi V HinksonCenter for Biomedical Informatics and Information Technology, National Cancer InstituteRockville, MD, United States.
Tanja M DavidsenCenter for Biomedical Informatics and Information Technology, National Cancer InstituteRockville, MD, United States.
Juli D KlemmCenter for Biomedical Informatics and Information Technology, National Cancer InstituteRockville, MD, United States.
Anthony R KerlavageCenter for Biomedical Informatics and Information Technology, National Cancer InstituteRockville, MD, United States.
Warren A KibbeCenter for Biomedical Informatics and Information Technology, National Cancer InstituteRockville, MD, United States.
Ishwar Chandramouliswaran
National Cancer Institute · USAmerican Association For The Advancement of Science · USNational Institute of Allergy and Infectious Diseases · US

Funding

CCR NIH HHS HHSN261200800001CNCI NIH HHS HHSN261200800001ENCI NIH HHS HHSN261201400006CNCI NIH HHS HHSN261201400007CNCI NIH HHS HHSN261201400008C
6 · The paper itself

Abstract

Advancements in next-generation sequencing and other -omics technologies are accelerating the detailed molecular characterization of individual patient tumors, and driving the evolution of precision medicine. Cancer is no longer considered a single disease, but rather, a diverse array of diseases wherein each patient has a unique collection of germline variants and somatic mutations. Molecular profiling of patient-derived samples has led to a data explosion that could help us understand the contributions of environment and germline to risk, therapeutic response, and outcome. To maximize the value of these data, an interdisciplinary approach is paramount. The National Cancer Institute (NCI) has initiated multiple projects to characterize tumor samples using multi-omic approaches. These projects harness the expertise of clinicians, biologists, computer scientists, and software engineers to investigate cancer biology and therapeutic response in multidisciplinary teams. Petabytes of cancer genomic, transcriptomic, epigenomic, proteomic, and imaging data have been generated by these projects. To address the data analysis challenges associated with these large datasets, the NCI has sponsored the development of the Genomic Data Commons (GDC) and three Cloud Resources. The GDC ensures data and metadata quality, ingests and harmonizes genomic data, and securely redistributes the data. During its pilot phase, the Cloud Resources tested multiple cloud-based approaches for enhancing data access, collaboration, computational scalability, resource democratization, and reproducibility. These NCI-led efforts are continuously being refined to better support open data practices and precision oncology, and to serve as building blocks of the NCI Cancer Research Data Commons.

Indexed as

big datacancercloud infrastructuregenomicsimagingprecision medicineproteomics

Identifiers

PMID28983483
PMCPMC5613113
OpenAlexW2757281736

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.