Evidence map›Paper›PMID 23761449›Full record

ArticleNucleic acids research2013

PosMed: Ranking genes and bioresources based on Semantic Web Association Study.

Yuko Makita, Norio Kobayashi, Yuko Yoshida, Koji Doi, Yoshiki Mochizuki, Koro Nishikata, Akihiro Matsushima, Satoshi Takahashi, Manabu Ishii, Terue Takatsuki and 5 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2013. 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. Article
  4. 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

15 authors.

Yuko MakitaBioinformatics and Systems Engineering Division, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
Norio Kobayashi
Yuko Yoshida
Koji Doi
Yoshiki Mochizuki
Koro Nishikata
Akihiro Matsushima
Satoshi Takahashi
Manabu Ishii
Terue Takatsuki
Rinki Bhatia
Zolzaya Khadbaatar
Hajime Watabe
Hiroshi Masuya
Tetsuro Toyoda

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Positional MEDLINE (PosMed; http://biolod.org/PosMed) is a powerful Semantic Web Association Study engine that ranks biomedical resources such as genes, metabolites, diseases and drugs, based on the statistical significance of associations between user-specified phenotypic keywords and resources connected directly or inferentially through a Semantic Web of biological databases such as MEDLINE, OMIM, pathways, co-expressions, molecular interactions and ontology terms. Since 2005, PosMed has long been used for in silico positional cloning studies to infer candidate disease-responsible genes existing within chromosomal intervals. PosMed is redesigned as a workbench to discover possible functional interpretations for numerous genetic variants found from exome sequencing of human disease samples. We also show that the association search engine enhances the value of mouse bioresources because most knockout mouse resources have no phenotypic annotation, but can be associated inferentially to phenotypes via genes and biomedical documents. For this purpose, we established text-mining rules to the biomedical documents by careful human curation work, and created a huge amount of correct linking between genes and documents. PosMed associates any phenotypic keyword to mouse resources with 20 public databases and four original data sets as of May 2013.

Indexed as

GenesPhenotypeSoftwareAnimalsDatabases, FactualData Interpretation, StatisticalExomeGenetic Association StudiesGenetic VariationHumansInternetMiceMice, Knockout

Identifiers

PMID23761449
PMCPMC3692089

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.