Evidence map›Paper›PMID 25418743›Full record

ArticleNature genetics2014

Predicting protein networks in cancer.

Andrea Califano

Abstract readComment
PubMed Publisher
In one paragraph

Article in Nature genetics, 2014. 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
–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

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Andrea CalifanoDepartment of Systems Biology, the Department of Biochemistry and Molecular Biophysics, and the Department of Biomedical Informatics at Columbia University, New York, New York, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Characterization of the mutational landscape of tumors is important to understanding disease etiology but does not provide mechanistic insight into the functional role of specific mutations. A new study introduces a statistical mechanical framework that draws on biophysical data from SH2 domain-phosphoprotein interactions to predict the functional effects of mutations in cancer.

Indexed as

Models, GeneticHumansNeoplasms

Identifiers

PMID25418743

What OpenQuestion holds

Textmetadata
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.