Evidence map›Paper›PMID 18053208›Full record

ArticleGenome biology2007

A new computational approach to analyze human protein complexes and predict novel protein interactions.

Sara Zanivan, Ilaria Cascone, Chiara Peyron, Ivan Molineris, Serena Marchio, Michele Caselle, Federico Bussolino

Open access · goldAbstract read
In one paragraph

Article in Genome biology, 2007. 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
0.1field-weighted citation impact, top 46% 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, 11 citations in OpenAlex.

  1. Article
  2. 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 at 3 institutions in 3 countries.

Sara ZanivanDepartment of Oncological Sciences and Division of Molecular Angiogenesis, Institute for Cancer Research and Treatment (IRCC), University of Torino Medical School, Strada Provinciale, I-10060 Candiolo (Turin), Italy. zanivan@biochem.mpg.de
Ilaria Cascone
Chiara Peyron
Ivan Molineris
Serena Marchio
Michele Caselle
Federico Bussolino
University of Turin · ITCandiolo Cancer Institute · ITMax Planck Institute of Biochemistry · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We propose a new approach to identify interacting proteins based on gene expression data. By using hypergeometric distribution and extensive Monte-Carlo simulations, we demonstrate that looking at synchronous expression peaks in a single time interval is a high sensitivity approach to detect co-regulation among interacting proteins. Combining gene expression and Gene Ontology similarity analyses enabled the extraction of novel interactions from microarray datasets. Applying this approach to p21-activated kinase 1, we validated alpha-tubulin and early endosome antigen 1 as its novel interactors.

Indexed as

Computational BiologyDatabases, ProteinGene Expression RegulationHeLa CellsHumansModels, GeneticMonte Carlo Methodp21-Activated KinasesProteinsSensitivity and SpecificitySoftwarep21-Activated KinasesPAK1 protein, humanProteins

Identifiers

PMID18053208
PMCPMC2246258
OpenAlexW2120122331

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.