Evidence map›Paper›PMID 19828070›Full record

ArticleBMC bioinformatics2009

Survival Online: a web-based service for the analysis of correlations between gene expression and clinical and follow-up data.

Luca Corradi, Valentina Mirisola, Ivan Porro, Livia Torterolo, Marco Fato, Paolo Romano, Ulrich Pfeffer

Abstract read
In one paragraph

Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Luca CorradiUniversity of Genoa, Department of Communication, Computer and System Sciences, Viale Causa 13, Genoa, Italy. luca.corradi@unige.it
Valentina Mirisola
Ivan Porro
Livia Torterolo
Marco Fato
Paolo Romano
Ulrich Pfeffer

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundComplex microarray gene expression datasets can be used for many independent analyses and are particularly interesting for the validation of potential biomarkers and multi-gene classifiers. This article presents a novel method to perform correlations between microarray gene expression data and clinico-pathological data through a combination of available and newly developed processing tools.

resultsWe developed Survival Online (available at http://ada.dist.unige.it:8080/enginframe/bioinf/bioinf.xml), a Web-based system that allows for the analysis of Affymetrix GeneChip microarrays by using a parallel version of dChip. The user is first enabled to select pre-loaded datasets or single samples thereof, as well as single genes or lists of genes. Expression values of selected genes are then correlated with sample annotation data by uni- or multi-variate Cox regression and survival analyses. The system was tested using publicly available breast cancer datasets and GO (Gene Ontology) derived gene lists or single genes for survival analyses.

conclusionThe system can be used by bio-medical researchers without specific computation skills to validate potential biomarkers or multi-gene classifiers. The design of the service, the parallelization of pre-processing tasks and the implementation on an HPC (High Performance Computing) environment make this system a useful tool for validation on several independent datasets.

Indexed as

Gene ExpressionSoftwareBreast NeoplasmsComputational BiologyFemaleFollow-Up StudiesGene Expression ProfilingHumansInternetUser-Computer Interface

Identifiers

PMID19828070
PMCPMC2762059

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Registered trials

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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.