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

MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information.

Computers in biology and medicine - 1 Sept 2021

Dai Qiuying, Chu Yanyi, Li Zhiqi, Zhao Yusong, Mao Xueying, Wang Yanjing, Xiong Yi, Wei Dong-Qing

Abstract excerpt

MicroRNAs (miRNAs) are significant regulators in various biological processes. They may become promising biomarkers or therapeutic targets, which provide a new perspective in diagnosis and treatment of multiple diseases. Since the experimental methods are always costly and resource-consuming, prediction of disease-related miRNAs using computational methods is in great need. In this study, we developed MDA-CF to...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Computational Biology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Forests

Share to Genetic Predisposition to Disease

Share to MicroRNAs

Share to Software

Source

Identifiers

PMID: 34371319
DOI: 10.1016/j.compbiomed.2021.104706

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

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

MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information.PMID 34371319Read ↗

Prediction of miRNA-disease associations based on PCA and cascade forest.Semantic proximity Read ↗

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

DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association.Semantic proximity Read ↗

Collective Prediction of Disease-Associated miRNAs Based on Transduction Learning.Semantic proximity Read ↗

Prediction of miRNA-disease associations by neural network-based deep matrix factorization.Semantic proximity Read ↗

A learning-based framework for miRNA-disease association identification using neural networksSemantic proximity Read ↗

Prediction of disease-related miRNAs by voting with multiple classifiers.Semantic proximity Read ↗

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

SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoostSemantic proximity Read ↗

Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion.Semantic proximity Read ↗

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

Hessian Regularized [Formula: see text]-Nonnegative Matrix Factorization and Deep Learning for miRNA-Disease Associations Prediction.Semantic proximity Read ↗

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