Evidence map›Paper›PMID 22096235›Full record

ArticleNucleic acids research2012

Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks.

Benjamin Haibe-Kains, Catharina Olsen, Amira Djebbari, Gianluca Bontempi, Mick Correll, Christopher Bouton, John Quackenbush

Abstract read
In one paragraph

Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
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  5. Review
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  10. Review
  11. Article
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  14. Article
  15. Estimation of Gene Regulatory Networks.Postdoc journal : a journal of postdoctoral research and postdoctoral affairs · 2013
    Article
  16. Article
  17. Article
  18. 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.

Benjamin Haibe-KainsDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA 02215, USA, USA. bhaibeka@jimmy.harvard.edu
Catharina Olsen
Amira Djebbari
Gianluca Bontempi
Mick Correll
Christopher Bouton
John Quackenbush

Funding

Integrated approaches to deriving predictive networks from public data sourcesR01LM010129 · NLM · DANA-FARBER CANCER INST · PI QUACKENBUSH, JOHN · 2009 to 2010
$1.4M
NLM NIH HHS 1R01LM010129NLM NIH HHS R01 LM010129
6 · The paper itself

Abstract

Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.

Indexed as

Databases, GeneticGene Regulatory NetworksGenomicsHumansInternetPhenotypeUser-Computer Interface

Identifiers

PMID22096235
PMCPMC3245161

What OpenQuestion holds

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LicenceCC BY-NC
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.