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

ANMDA: anti-noise based computational model for predicting potential miRNA-disease associations.

BMC bioinformatics - 2 Jul 2021

Chen Xue-Jun, Hua Xin-Yun, Jiang Zhen-Ran

Abstract excerpt

BACKGROUND: A growing proportion of research has proved that microRNAs (miRNAs) can regulate the function of target genes and have close relations with various diseases. Developing computational methods to exploit more potential miRNA-disease associations can provide clues for further functional research. RESULTS: Inspired by the work of predecessors, we discover that the noise hiding in the data can affect the...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Area Under Curve
Share to Computational Biology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Predisposition to Disease

Share to MicroRNAs

Share to ROC Curve

Source

Open in PubMed Open full text

Identifiers

PMID: 34215183
DOI: 10.1186/s12859-021-04266-6
PMCID: PMC8254275

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

ANMDA: anti-noise based computational model for predicting potential miRNA-disease associations.PMID 34215183Read ↗

EGBMMDA: Extreme Gradient Boosting Machine for MiRNA-Disease Association prediction.Semantic proximity Read ↗

A consistent evaluation of miRNA-disease association prediction modelsSemantic proximity Read ↗

Identification of miRNA-disease associations via deep forest ensemble learning based on autoencoder.Semantic proximity Read ↗

Predicting potential miRNA-disease associations based on more reliable negative sample selection.Semantic proximity Read ↗

From biogenesis to deep modeling: a holistic review of miRNA-disease prediction computational methods with experimental comparison.Semantic proximity Read ↗

WBSMDA: Within and Between Score for MiRNA-Disease Association prediction.Semantic proximity Read ↗

RBMMMDA: predicting multiple types of disease-microRNA associations.Semantic proximity Read ↗

Predicting miRNA-disease associations using an ensemble learning framework with resampling method.Semantic proximity Read ↗

DAESTB: inferring associations of small molecule-miRNA via a scalable tree boosting model based on deep autoencoder.Semantic proximity Read ↗

A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions.Semantic proximity Read ↗

MDSCMF: Matrix Decomposition and Similarity-Constrained Matrix Factorization for miRNA-Disease Association Prediction.Semantic proximity Read ↗

DAE-CFR: detecting microRNA-disease associations using deep autoencoder and combined feature representation.Semantic proximity Read ↗

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