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

DNRLCNN: A CNN Framework for Identifying MiRNA-Disease Associations Using Latent Feature Matrix Extraction with Positive Samples.

Interdisciplinary sciences, computational life sciences - 1 Jun 2022

Zhong Jiancheng, Zhou Wubin, Kang Jiedong, Fang Zhuo, Xie Minzhu, Xiao Qiu, Peng Wei

Abstract excerpt

Emerging evidence indicates that miRNAs have strong relationships with many human diseases. Investigating the associations will contribute to elucidating the activities of miRNAs and pathogenesis mechanisms, and providing new opportunities for disease diagnosis and drug discovery. Therefore, it is of significance to identify potential associations between miRNAs and diseases. The existing databases about 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 Computational Biology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Predisposition to Disease

Share to MicroRNAs

Share to Neural Networks, Computer

Source

Identifiers

PMID: 35428965
DOI: 10.1007/s12539-022-00509-z

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

DNRLCNN: A CNN Framework for Identifying MiRNA-Disease Associations Using Latent Feature Matrix Extraction with Positive Samples.PMID 35428965Read ↗

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

NMCMDA: neural multicategory MiRNA-disease association prediction.Semantic proximity Read ↗

DMFLDA: A Deep Learning Framework for Predicting lncRNA-Disease Associations.Semantic proximity Read ↗

Neural inductive matrix completion with graph convolutional networks for miRNA-disease association predictionSemantic proximity Read ↗

FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks.Semantic proximity Read ↗

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

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

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

Deep-belief network for predicting potential miRNA-disease associations.Semantic proximity Read ↗

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

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

CCLDA: prediction of lncRNA-disease associations based on Convolutional Block Attention Module and Capsule Network.Semantic proximity Read ↗

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