Evidence map›Paper›PMID 23957008›Full record

ReviewBioMed research international2013

Translational bioinformatics for diagnostic and prognostic prediction of prostate cancer in the next-generation sequencing era.

Jiajia Chen, Daqing Zhang, Wenying Yan, Dongrong Yang, Bairong Shen

Abstract readReview
In one paragraph

Review in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 1 pooled it
–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

44 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  19. International journal of molecular sciences · 2023
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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

5 authors.

Jiajia ChenCenter for Systems Biology, Soochow University, Suzhou 215006, China.
Daqing Zhang
Wenying Yan
Dongrong Yang
Bairong Shen

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The discovery of prostate cancer biomarkers has been boosted by the advent of next-generation sequencing (NGS) technologies. Nevertheless, many challenges still exist in exploiting the flood of sequence data and translating them into routine diagnostics and prognosis of prostate cancer. Here we review the recent developments in prostate cancer biomarkers by high throughput sequencing technologies. We highlight some fundamental issues of translational bioinformatics and the potential use of cloud computing in NGS data processing for the improvement of prostate cancer treatment.

Indexed as

High-Throughput Nucleotide SequencingProtein BiosynthesisComputational BiologyGenome, HumanGenomicsHumansMalePrognosisProstatic Neoplasms

Identifiers

PMID23957008
PMCPMC3727129

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.