Evidence map›Paper›PMID 24932637›Full record

ArticlePloS one2014

SemFunSim: a new method for measuring disease similarity by integrating semantic and gene functional association.

Liang Cheng, Jie Li, Peng Ju, Jiajie Peng, Yadong Wang

Open access · goldAbstract readComparative Study
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed
5.4field-weighted citation impact, top 3% 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

55 citing papers in PubMed, 143 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Hessian RegularizedInterdisciplinary sciences, computational life sciences · 2024
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. mBMC bioinformatics · 2021
    Article
  17. Article
  18. Article
  19. Prediction of Gastric Cancer-Related Proteins Based on Graph Fusion Method.Frontiers in cell and developmental biology · 2021
    Article
  20. 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

5 authors at 2 institutions in 2 countries.

Liang ChengCenter for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Jie LiCenter for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Peng JuSchool of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
Jiajie PengCenter for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Yadong WangCenter for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Heilongjiang Institute of Technology · CNNanyang Technological University · SG

Funding

Alcohol dependence and brain endocannabinoid functionR01AA020404 · NIAAA · SCRIPPS RESEARCH INSTITUTE, THE · PI MARTIN-FARDON, REMI · 2011 to 2015
$2.2M
NIAAA NIH HHS R01 AA020404
6 · The paper itself

Abstract

backgroundMeasuring similarity between diseases plays an important role in disease-related molecular function research. Functional associations between disease-related genes and semantic associations between diseases are often used to identify pairs of similar diseases from different perspectives. Currently, it is still a challenge to exploit both of them to calculate disease similarity. Therefore, a new method (SemFunSim) that integrates semantic and functional association is proposed to address the issue.

methodsSemFunSim is designed as follows. First of all, FunSim (Functional similarity) is proposed to calculate disease similarity using disease-related gene sets in a weighted network of human gene function. Next, SemSim (Semantic Similarity) is devised to calculate disease similarity using the relationship between two diseases from Disease Ontology. Finally, FunSim and SemSim are integrated to measure disease similarity.

resultsThe high average AUC (area under the receiver operating characteristic curve) (96.37%) shows that SemFunSim achieves a high true positive rate and a low false positive rate. 79 of the top 100 pairs of similar diseases identified by SemFunSim are annotated in the Comparative Toxicogenomics Database (CTD) as being targeted by the same therapeutic compounds, while other methods we compared could identify 35 or less such pairs among the top 100. Moreover, when using our method on diseases without annotated compounds in CTD, we could confirm many of our predicted candidate compounds from literature. This indicates that SemFunSim is an effective method for drug repositioning.

Indexed as

AlgorithmsGene Regulatory NetworksArea Under CurveBenchmarkingBiological OntologiesComputational BiologyDatabases, FactualDiseaseDrug DiscoveryDrug RepositioningGene OntologyHumansROC CurveSemantics

Identifiers

PMID24932637
PMCPMC4059643
OpenAlexW2143076797

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.