Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction.

BioMed research international - 1 Jan 2018

Li Xiaoying, Lin Yaping, Gu Changlong, Li Zejun

Abstract excerpt

Aberrant expression of microRNAs (miRNAs) can be applied for the diagnosis, prognosis, and treatment of human diseases. Identifying the relationship between miRNA and human disease is important to further investigate the pathogenesis of human diseases. However, experimental identification of the associations between diseases and miRNAs is time-consuming and expensive. Computational methods are efficient...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Computational Biology
Share to Genetic Association Studies

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Predisposition to Disease

Share to MicroRNAs

Share to Prognosis

Share to Risk Factors

Source

Open in PubMed Open full text

Identifiers

PMID: 29750163
DOI: 10.1155/2018/5747489
PMCID: PMC5884242

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction.PMID 29750163Read ↗

NARRMDA: negative-aware and rating-based recommendation algorithm for miRNA-disease association prediction.Semantic proximity Read ↗

Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering.Semantic proximity Read ↗

HAMDA: Hybrid Approach for MiRNA-Disease Association prediction.Semantic proximity Read ↗

SNMDA: A novel method for predicting microRNA-disease associations based on sparse neighbourhood.Semantic proximity Read ↗

IMIPMF: Inferring miRNA-disease interactions using probabilistic matrix factorization.Semantic proximity Read ↗

Inferring MicroRNA-Disease Associations Based on the Identification of a Functional Module.Semantic proximity Read ↗

A novel computational model based on super-disease and miRNA for potential miRNA-disease association prediction.Semantic proximity Read ↗

Predicting MicroRNA-Disease Associations Based on Improved MicroRNA and Disease Similarities.Semantic proximity Read ↗

MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations.Semantic proximity Read ↗

MDAlmc: A Novel Low-rank Matrix Completion Model for MiRNADisease Association Prediction by Integrating Similarities among MiRNAs and Diseases.Semantic proximity Read ↗

Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization.Semantic proximity Read ↗

MicroRNA-disease association prediction by matrix tri-factorization.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.