Evidence map›Paper›PMID 30598090›Full record

ArticleBMC systems biology2018

FMSM: a novel computational model for predicting potential miRNA biomarkers for various human diseases.

Yiwen Sun, Zexuan Zhu, Zhu-Hong You, Zijie Zeng, Zhi-An Huang, Yu-An Huang

Open access · diamondAbstract read
In one paragraph

Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 32% 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

4 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Experimental validation of a predicted microRNA within humanMolecular biology research communications · 2021
    Article
  3. Predicting lncRNA-miRNA InteractionFrontiers in genetics · 2019
    Article
  4. 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

6 authors at 4 institutions in 2 countries.

Yiwen SunSchool of Medicine, Shenzhen University, Shenzhen, 518060, China.
Zexuan ZhuCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.
Zhu-Hong YouXinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, ürümqi, 830011, China.
Zijie ZengCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.
Zhi-An HuangDepartment of Computer Science, City University of Hong Kong, Hong Kong, 999077, China. huang_za@outlook.com.
Yu-An HuangDepartment of Computing, Hong Kong Polytechnic University, Hong Kong, 999077, China. yahaung1991@gmail.com.
Shenzhen University · CNCity University of Hong Kong · HKHong Kong Polytechnic University · HKXinjiang Technical Institute of Physics & Chemistry · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicroRNA (miRNA) plays a key role in regulation mechanism of human biological processes, including the development of disease and disorder. It is necessary to identify potential miRNA biomarkers for various human diseases. Computational prediction model is expected to accelerate the process of identification.

resultsConsidering the limitations of previously proposed models, we present a novel computational model called FMSM. It infers latent miRNA biomarkers involved in the mechanism of various diseases based on the known miRNA-disease association network, miRNA expression similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. FMSM achieves reliable prediction performance in 5-fold and leave-one-out cross validations with area under ROC curve (AUC) values of 0.9629+/- 0.0127 and 0.9433, respectively, which outperforms the state-of-the-art competitors and classical algorithms. In addition, 19 of top 25 predicted miRNAs have been validated to have associations with Colonic Neoplasms in case study.

conclusionsA factored miRNA similarity based model and miRNA expression similarity substantially contribute to the well-performing prediction. The list of the predicted most latent miRNA biomarkers of various human diseases is publicized. It is anticipated that FMSM could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates.

Indexed as

Computer SimulationComputational BiologyDiseaseGenetic MarkersHumansMicroRNAsGenetic MarkersMicroRNAsBiomarkerComputational predictionExpression profilesmiRNA-disease association

Identifiers

PMID30598090
PMCPMC6311922
OpenAlexW2907670139

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.