Evidence map›Paper›PMID 24574114›Full record

ArticleBioinformatics (Oxford, England)2014

Optimization of gene set annotations via entropy minimization over variable clusters (EMVC).

H Robert Frost, Jason H Moore

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Spectral gene set enrichment (SGSE).BMC bioinformatics · 2015
    Article
  4. An Independent Filter for Gene Set Testing Based on Spectral Enrichment.IEEE/ACM transactions on computational biology and bioinformatics
    Article
  5. 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

2 authors.

H Robert FrostDepartments of Genetics and Community and Family Medicine, Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH 03755, USA.
Jason H MooreDepartments of Genetics and Community and Family Medicine, Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH 03755, USA.

Funding

Translational Engineering in Cancer (TEC)P30CA023108 · NCI · DARTMOUTH COLLEGE · PI Steven D Leach · 1985 to 2026
$91.3M
Ultrastructural analysis of axonal Kv2-mediated endoplasmic reticulum/plasma membrane (ER/PM) junctionsP20GM103506 · NIGMS · DARTMOUTH COLLEGE · PI STEVEN FIERING · 2012 to 2026
$60.1M
Quantitative Biology Research InstituteP20GM103534 · NIGMS · DARTMOUTH COLLEGE · PI AMOS, CHRISTOPHER I. · 2012 to 2016
$8.6M
Bioinformatics Strategies for Genome-Wide Association StudiesR01LM010098 · NLM · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H., WILLIAMS, SCOTT MATTHEW · 2009 to 2023
$5.1M
Bioinformatics Strategies for Multidimensional Brain Imaging GeneticsR01LM011360 · NLM · INDIANA UNIVERSITY INDIANAPOLIS · PI MOORE, JASON H., SAYKIN, ANDREW J · 2012 to 2015
$1.4M
Bioinformatics Approaches to Visual Disease GeneticsR01EY022300 · NEI · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H. · 2012 to 2014
$947k
NCI NIH HHS P30 CA023108NEI NIH HHS EY022300NEI NIH HHS R01 EY022300NIGMS NIH HHS GM103506NIGMS NIH HHS GM103534NIGMS NIH HHS P20 GM103506NIGMS NIH HHS P20 GM103534NLM NIH HHS LM010098NLM NIH HHS LM011360NLM NIH HHS R01 LM010098NLM NIH HHS R01 LM011360
6 · The paper itself

Abstract

motivationGene set enrichment has become a critical tool for interpreting the results of high-throughput genomic experiments. Inconsistent annotation quality and lack of annotation specificity, however, limit the statistical power of enrichment methods and make it difficult to replicate enrichment results across biologically similar datasets.

resultsWe propose a novel algorithm for optimizing gene set annotations to best match the structure of specific empirical data sources. Our proposed method, entropy minimization over variable clusters (EMVC), filters the annotations for each gene set to minimize a measure of entropy across disjoint gene clusters computed for a range of cluster sizes over multiple bootstrap resampled datasets. As shown using simulated gene sets with simulated data and Molecular Signatures Database collections with microarray gene expression data, the EMVC algorithm accurately filters annotations unrelated to the experimental outcome resulting in increased gene set enrichment power and better replication of enrichment results. AVAILABILITY AND IMPLEMENTATION: http://cran.r-project.org/web/packages/EMVC/index.html.

Indexed as

AlgorithmsGene Expression ProfilingMolecular Sequence AnnotationHumansLeukemiaOligonucleotide Array Sequence AnalysisTumor Suppressor Protein p53Tumor Suppressor Protein p53

Identifiers

PMID24574114
PMCPMC4058919

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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.