Evidence map›Paper›PMID 24103636›Full record

ArticleJournal of biomedical semantics2013

Enrichment analysis applied to disease prognosis.

Catia M Machado, Ana T Freitas, Francisco M Couto

Open access · goldAbstract read
In one paragraph

Article in Journal of biomedical semantics, 2013. 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
0.4field-weighted citation impact, top 31% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
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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

3 authors at 3 institutions in 1 country.

Catia M MachadoLaSIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal. cmachado@xldb.di.fc.ul.pt.
Ana T Freitas
Francisco M Couto
Institute for Systems Engineering and Computers · PTInstituto Superior TécnicoUniversity of Lisbon · PT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

: Enrichment analysis is well established in the field of transcriptomics, where it is used to identify relevant biological features that characterize a set of genes obtained in an experiment.This article proposes the application of enrichment analysis as a first step in a disease prognosis methodology, in particular of diseases with a strong genetic component. With this analysis the objective is to identify clinical and biological features that characterize groups of patients with a common disease, and that can be used to distinguish between groups of patients associated with disease-related events. Data mining methodologies can then be used to exploit those features, and assist medical doctors in the evaluation of the patients in respect to their predisposition for a specific event.In this work the disease hypertrophic cardiomyopathy (HCM) is used as a case-study, as a first test to assess the feasibility of the application of an enrichment analysis to disease prognosis. To perform this assessment, two groups of patients have been considered: patients that have suffered a sudden cardiac death episode and patients that have not.The results presented were obtained with genetic data and the Gene Ontology, in two enrichment analyses: an enrichment profiling aiming at characterizing a group of patients (e.g. that suffered a disease-related event) based on their mutations; and a differential enrichment aiming at identifying differentiating features between a sub-group of patients and all the patients with the disease. These analyses correspond to an adaptation of the standard enrichment analysis, since multiple sets of genes are being considered, one for each patient.The preliminary results are promising, as the sets of terms obtained reflect the current knowledge about the gene functions commonly altered in HCM patients, thus allowing their characterization. Nevertheless, some factors need to be taken into consideration before the full potential of the enrichment analysis in the prognosis methodology can be evaluated. One of such factors is the need to test the enrichment analysis with clinical data, in addition to genetic data, since both types of data are expected to be necessary for prognosis purposes.

Identifiers

PMID24103636
PMCPMC4126066
OpenAlexW2068373991

What OpenQuestion holds

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LicenceCC BY
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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.