DRMDA statistics

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Citations per year

Number of citations per year for the bioinformatics software tool DRMDA

Tool usage distribution map

This map represents all the scientific publications referring to DRMDA per scientific context
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Associated diseases


Popular tool citations

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DRMDA specifications


Unique identifier OMICS_22558
Alternative name deep representations-based miRNA-disease association


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Publication for deep representations-based miRNA-disease association

DRMDA citations


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

Biomed Res Int
PMCID: 5884242
PMID: 29750163
DOI: 10.1155/2018/5747489

[…] mework to predict potential miRNA-disease associations using weighted k nearest neighbor profiles to incorporate miRNA similarity and disease matrices. Chen et al. [] presented a computational method DRMDA based on stacked autoencoder, greedy layer-wise unsupervised pretraining algorithm and SVM, and this method was implemented to predict potential miRNA-disease associations. However, DRMDA result […]


A deep ensemble model to predict miRNA disease association

Sci Rep
PMCID: 5670180
PMID: 29101378
DOI: 10.1038/s41598-017-15235-6

[…] sociations and further improve the performance. Furthermore, Li et al. applied a low-rank matrix recovery method to uncover missing miRNA-disease associations, and Chen et al. proposed a model called DRMDA that utilized a sparse auto-encoder to obtain the representation of miRNA and disease and then chose a SVM classifier to predict miRNA-disease associations.Previous computational models were lim […]

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DRMDA institution(s)
School of Information and Control Engineering, China; University of Mining and Technology, Xuzhou, China; School of Life Science, Peking University, Beijing, China; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China; School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
DRMDA funding source(s)
Supported by National Natural Science Foundation of China under Grant No. 11631014 and No. 61572506 and by Pioneer Hundred Talents Program of Chinese Academy of Sciences.

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