MIDP statistics

info info

Citations per year

Number of citations per year for the bioinformatics software tool MIDP
info

Tool usage distribution map

This map represents all the scientific publications referring to MIDP per scientific context
info info

Associated diseases

info

Popular tool citations

chevron_left miRNA-disease association prediction chevron_right
Want to access the full stats & trends on this tool?

MIDP specifications

Information


Unique identifier OMICS_27617
Name MIDP
Alternative name miRNAs associated with Diseases Prediction
Interface Web user interface
Restrictions to use None
Computer skills Basic
Maintained No

Maintainers


This tool is not available anymore.

Publication for miRNAs associated with Diseases Prediction

MIDP citations

 (7)
library_books

Global Similarity Method Based on a Two tier Random Walk for the Prediction of microRNA–Disease Association

2018
Sci Rep
PMCID: 5915491
PMID: 29691434
DOI: 10.1038/s41598-018-24532-7

[…] disease association among all diseases. No negative samples are needed in this method, and it can be applied to predict isolated diseases and new miRNAs.Xuan et al. designed a computation model named MIDP based on random walk algorithm. This algorithm walks in a two-tier network composed of the disease similarity, miRNA similarity, and known miRNA–disease association. This model can predict diseas […]

library_books

Prediction of microRNA disease associations based on distance correlation set

2018
BMC Bioinformatics
PMCID: 5905221
PMID: 29665774
DOI: 10.1186/s12859-018-2146-x

[…] HDMP to predict potential disease-miRNA associations based on weighted k most similar neighbours [] and developed a method for predicting potential disease-associated microRNAs based on random walk (MIDP) []. Chen et al. proposed a prediction method called RWRMDA by implementing random walk on the miRNA functional similarity network and further proposed a model called RLSMDA based on semi-supervi […]

library_books

NDAMDA: Network distance analysis for MiRNA‐disease association prediction

2018
J Cell Mol Med
PMCID: 5908143
PMID: 29532987
DOI: 10.1111/jcmm.13583

[…] etween a miRNA and a disease, an iterative process was carried out on the heterogeneous graph, summarizing all paths between the miRNA and the disease with the length equal to 3. Xuan et al developed MIDP to predict potential miRNA candidates for the diseases with known related miRNAs and MIDPE for the diseases without any known related miRNAs. It is worth mentioning that the negative effect of no […]

library_books

EGBMMDA: Extreme Gradient Boosting Machine for MiRNA Disease Association prediction

2018
PMCID: 5849212
PMID: 29305594
DOI: 10.1038/s41419-017-0003-x

[…] le evaluation outcomes were obtained from both cross-validations (LOOCV and fivefold) and case studies on CN, Lymphoma, PN, BN, and EN. EGBMMDA outperformed eight earlier models MiRAI, MCMDA, HGIMDA, MIDP, WBSMDA, RLSMDA, HDMP, and RWRMDA. We believe that it is the first decision tree learning-based computational model applied to predicting potential miRNA–disease associations.Three factors contri […]

library_books

EPMDA: an expression profile based computational model for microRNA disease association prediction

2017
Oncotarget
PMCID: 5675613
PMID: 29152061
DOI: 10.18632/oncotarget.18788

[…] A-disease associations, which can be mainly classified into three categories. The first category is mainly based on network similarity measurement. For example, Xuan et al. have proposed the model of MIDP which is mainly based on the assumption that functionally similar microRNAs tend to be involved in similar diseases []. Specifically, MIDP model constructs a microRNA functional similarity networ […]

library_books

Computational prediction of human disease related microRNAs by path based random walk

2017
Oncotarget
PMCID: 5601672
PMID: 28938576
DOI: 10.18632/oncotarget.17226

[…] walk with restart method. It also based on an integration of data similarities such as diseases and lncRNA, and applying a random walk to predict novel lncRNA-disease.In 2015, Xuan et al. [] proposed MIDP, a predictive model for disease-related miRNA. In this method, the prediction process is modeled as random walk on a miRNA network that is derived from miRNA-associated diseases. For a specific d […]


Want to access the full list of citations?
MIDP institution(s)
School of Computer Science and Technology, Heilongjiang University, Harbin, China; School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China; School of Information Science and Technology, Heilongjiang University, Harbin, China; College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
MIDP funding source(s)
Supported by the Natural Science Foundation of China (61302139, 61402138), China Postdoctoral Science Foundation (2014M550200, 2014M561350), the Science and Technology Innovation Team Construction Project of Heilongjiang Province College (2013TD012), the Natural Science Foundation of Heilongjiang Province (F201324, E201452), the Postdoctoral Foundation of Heilongjiang Province (LBHZ14152), the Young Innovative Talent Research Foundation of Harbin Science and Technology Bureau (2012RFQXS094), the Support Program for Young Academic Key Teacher of Higher Education of Heilongjiang Province (1254G030), and the Distinguished Youth Foundation of Heilongjiang University (JCL201405).

MIDP reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review MIDP