Evidence map›Paper›PMID 36370350›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

Utilizing Public Molecular Biological Databases for CCN Family Research.

Satoshi Kubota

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. CCN proteins: opportunities for clinical studies-a personal perspective.Journal of cell communication and signaling · 2023
    Review
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

1 author.

Satoshi KubotaDepartment of Biochemistry and Molecular Dentistry, Okayama University Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan. kubota1@md.okayama-u.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Classically, scientific research has been driven by hypotheses based on personal inspiration and intuition against the background of personal knowledge. In contrast, scientists have recently proposed that scientific research should basically be driven by data, meaning big data yielded by preliminary omic analyses in this context. A genuine hypothesis-driven strategy is usually exciting but occasionally ends up with negative conclusions, whereas a data-driven approach is less exciting and cost-consuming but produces significant outcomes in most cases. Here, we should be aware that a number of bioscientific resources provide a variety of big data free of charge. Therefore, one of the most effective research strategies is to construct a research question based on comprehensive knowledge derived not only from inside information, but also from the analysis of data available to everybody. However, a classical scientist without a sufficient bioinformatic background may hesitate in dealing with information supplied through the Internet. This chapter is aimed at CCN family researchers who do not possess specific bioinformatic knowledge and/or huge grants-in-aid, in order to assist them in developing their research by taking advantage of the scientific treasury open to the public.

Indexed as

Computational BiologyProteinsDatabases, ChemicalFamilyResearch DesignProteinsbig databioinformaticsCCN familydatabaseomics

Identifiers

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.