Evidence map›Paper›PMID 32099411›Full record

ReviewStem cells and cloning : advances and applications2020

Informatics Approaches for Harmonized Intelligent Integration of Stem Cell Research.

Joseph Finkelstein, Irena Parvanova, Frederick Zhang

Abstract readReview
In one paragraph

Review in Stem cells and cloning : advances and applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Towards an Open-Source Platform to Harmonize and Share Research Results from COVID-19 Interventional and Observational Studies.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023
    Article
  2. Review
  3. A Web-based Platform to Share Harmonized Results from COVID-19 Clinical Studies.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022
    Article
  4. Article
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

3 authors.

Joseph FinkelsteinDepartment of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID 0000-0002-8084-7441
Irena ParvanovaDepartment of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID 0000-0002-1861-193X
Frederick ZhangCenter for Bioinformatics and Data Analytics, Columbia University, New York, NY, USA.ORCID 0000-0002-4053-6657

Funding

MSHS Translational Science HubUL1TR001433 · NCATS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI WRIGHT, ROSALIND J · 2015 to 2019
$26.6M
Enhancing the Development of New Regenerative Medicine Therapies:Proposal to Build a Robust Clinical Research Development and Data HubOT2HL147606 · NHLBI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI FINKELSTEIN, JOSEPH E · 2018 to 2019
$1.8M
NCATS NIH HHS UL1 TR001433NHLBI NIH HHS OT2 HL147606
6 · The paper itself

Abstract

As biomedical data integration and analytics play an increasing role in the field of stem cell research, it becomes important to develop ways to standardize, aggregate, and share data among researchers. For this reason, many databases have been developed in recent years in an attempt to systematically warehouse data from different stem cell projects and experiments at the same time. However, these databases vary widely in their implementation and structure. The aim of this scoping review is to characterize the main features of available stem cell databases in order to identify specifications useful for implementation in future stem cell databases. We conducted a scoping review of peer-reviewed literature and online resources to identify and review available stem cell databases. To identify the relevant databases, we performed a PubMed search using relevant MeSH terms followed by a web search for databases which may not have an associated journal article. In total, we identified 16 databases to include in this review. The data elements reported in these databases represented a broad spectrum of parameters from basic socio-demographic variables to various cells characteristics, cell surface markers expression, and clinical trial results. Three broad sets of functional features that provide utility for future stem cell research and facilitate bioinformatics workflows were identified. These features consisted of the following: common data elements, data visualization and analysis tools, and biomedical ontologies for data integration. Stem cell bioinformatics is a quickly evolving field that generates a growing number of heterogeneous data sets. Further progress in the stem cell research may be greatly facilitated by development of applications for intelligent stem cell data aggregation, sharing and collaboration process.

Indexed as

databasesdata integrationstem cells

Identifiers

PMID32099411
PMCPMC6996484

What OpenQuestion holds

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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.