Evidence map›Paper›PMID 27191267›Full record

ArticleOncotarget2016

Identification of polycystic ovary syndrome potential drug targets based on pathobiological similarity in the protein-protein interaction network.

Hao Huang, Yuehan He, Wan Li, Wenqing Wei, Yiran Li, Ruiqiang Xie, Shanshan Guo, Yahui Wang, Jing Jiang, Binbin Chen and 4 more

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Oncotarget, 2016. 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
1.3field-weighted citation impact, top 16% 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

5 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. In silico Methods for Identification of Potential Therapeutic Targets.Interdisciplinary sciences, computational life sciences · 2022
    Review
  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

14 authors at 3 institutions in 1 country.

Hao HuangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Yuehan HeCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Wan LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Wenqing WeiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Yiran LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Ruiqiang XieCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Shanshan GuoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Yahui WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Jing JiangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Binbin ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Junjie LvCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Nana ZhangDepartment of Endocrinology and Metabolism Laboratory, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
Lina ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Weiming HeInstitute of Opto-electronics, Harbin Institute of Technology, Harbin, Heilongjiang Province, China.
Harbin Medical University · CNHarbin Institute of Technology · CNSecond Affiliated Hospital of Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polycystic ovary syndrome (PCOS) is one of the most common endocrinological disorders in reproductive aged women. PCOS and Type 2 Diabetes (T2D) are closely linked in multiple levels and possess high pathobiological similarity. Here, we put forward a new computational approach based on the pathobiological similarity to identify PCOS potential drug target modules (PPDT-Modules) and PCOS potential drug targets in the protein-protein interaction network (PPIN). From the systems level and biological background, 1 PPDT-Module and 22 PCOS potential drug targets were identified, 21 of which were verified by literatures to be associated with the pathogenesis of PCOS. 42 drugs targeting to 13 PCOS potential drug targets were investigated experimentally or clinically for PCOS. Evaluated by independent datasets, the whole PPDT-Module and 22 PCOS potential drug targets could not only reveal the drug response, but also distinguish the statuses between normal and disease. Our identified PPDT-Module and PCOS potential drug targets would shed light on the treatment of PCOS. And our approach would provide valuable insights to research on the pathogenesis and drug response of other diseases.

Indexed as

Protein Interaction MapsAdultComputer SimulationDiabetes Mellitus, Type 2Drug DesignFemaleHumansInsulin ResistanceModels, StatisticalPolycystic Ovary SyndromeProtein Interaction MappingReproducibility of ResultsROC CurveSensitivity and SpecificitySupport Vector Machinedrug targetmodulepathobiological similaritypolycystic ovary syndromeprotein-protein interaction network

Identifiers

PMID27191267
PMCPMC5122359
OpenAlexW2368319056

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.