DL-miRNA-disease-association-methods

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# 🏎💨miRNA-disease association prediction methods <a href='https:/DL-miRNA-disease-association-methods.r-lib.org'></a>
# A survey of deep learning for detecting miRNA-disease associations: databases, computational methods, challenges, and future directions
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## Paper [**[Paper link]**](https://doi.org/10.1109/TCBB.2024.3351752 "Click")

## What is miRNA?
RNA can be divided into two categories based on its coding function: (1) RNAs with coding potential, and (2) RNAs without coding potential, also known as non-coding RNA (ncRNA), which includes microRNAs (miRNA), snoRNAs, circRNAs and lncRNAs. Long non-coding RNAs (lncRNAs) are a major class of important ncRNAs with the lengths more than 200 nucleotides. An increasing number of lncRNA have been found to be abnormally expressed in human diseases, and play a critical role in tumor development.

## Table of Contents
* [Overview](#Overview) 
* [Data resources](#Data-resources) 
  - [miRNA-disease association data resources](#miRNA-disease-association-data-resources)
  - [miRNA-related data resourcess](#miRNA-related-data-resources)
  - [Disease-related data resources](#Disease-related-data-resources)
* [Classical deep learning models for predicting MDAs](#Classical-deep-learning-models-for-predicting-MDAs) 
  - [Autoencoder](#Autoencoder)
  - [Multi-layer perceptron](#Multi-layer-perceptron)
  - [Convolutional neural network](#Convolutional-neural-network)
* [Graph neural network-based methods for predicting MDAs](#Graph-neural-network-based-methods-for-predicting-MDAs) 
  - [Graph convolutional network](#Graph-convolutional-network)
  - [Graph attention network](#Graph-attention-network)
  - [Graph autoencoder](#Graph-autoencoder)
* [Other deep learning methonds](#Other-deep-learning-methonds) 
* [A Summary of Methodology Details for predicting MDAs](#A-Summary-of-Methodology-Details-for-predicting-MDAs) 
* [Cite](#Cite)
* [ Welcome to contribute](#Welcome-to-contribute)
  
## Overview
* We collect miRNA- and disease-related databases for MDA prediction, including miRNA-disease association databases, miRNA-related databases, and disease-related databases. 
* We provide the first a comprehensive overview of 45 MDA prediction models based on deep learning and graph neural networks. We classify the two types of models in detail, deep learning models such as autoencoder, multi-layer perceptron, convolutional neural network, variational autoencoder, gated recurrent unit, and generative adversarial network; graph neural network models such as graph convolutional network, graph attention network, graph autoencoder, as shown below figure 1.
![image](https://user-images.githubusercontent.com/95516781/195086616-534b5b54-beac-42ac-b0a0-9bdf66b73597.png)

Fig 1: Deep learning and Graph neural network computational methods for MDA prediction.

### Disease-related data resources
| Database  | Description  | URL | 
|:------------------:|:-----:|:---------------: |
|MeSH   | Is the NLM controlled vocabulary thesaurus used for indexing, cataloging, and searching of biomedical and health-related information  | http://www.nlm.nih.gov/ |
|HPO     |Offers a comprehensive logical standard to depict and computationally analyze phenotypic abnormalities within human disease |https://hpo.jax.org/app/ |
|OMIM  |Records collated information about genes and genetic phenotypes and the relations between them, with 26,588 entries covering 7,248 diseases and 4,685 genes|http://www.ncbi.nlm.nih.gov/omim  |
|DisGeNet   |Contains publicly available collections of genes and variants related to human diseases|https://www.disgenet.org/|
|LncRNADisease|Provides experimentally verified and predicted lncRNA-disease associations and circRNA-disease associations, as well as regulatory relationships between lncRNAs, mRNAs, and miRNAs| http://www.rnanut.net/lncrnadisease/|

## Classical deep learning models for predicting MDAs
### Autoencoder
1. **[DeepMDA]** Fu L, Peng Q. A deep ensemble model to predict miRNA-disease association, Scientific Reports 2017;7(1):14482. [**[Download]**](https://www.nature.com/articles/s41598-017-15235-6 "Click") [**[Code]**](https://github.com/sperfu/DeepMDA "Click")

2. **[DRMLDA]** Chen X, Gong Y, Zhang D-H et al. DRMDA: deep representations-based miRNA–disease association prediction, Journal of Cellular and Molecular Medicine 2018;22(1):472-485. [**[Download]**](https://onlinelibrary.wiley.com/doi/full/10.1111/jcmm.13336 "Click")

3. **[MLMDA]** Zheng K, You Z-H, Wang L et al. MLMDA: a machine learning approach to predict and validate MicroRNA–disease associations by integrating of heterogenous information sources, Journal of Translational Medicine 2019;17(1):260. [**[Download]**](https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-019-2009-x "Click")

4. **[DFELMDA]** Liu W, Lin H, Huang L et al. Identification of miRNA–disease associations via deep forest ensemble learning based on autoencoder, Briefings in Bioinformatics 2022;23(3). [**[Download]**](https://academic.oup.com/bib/article-abstract/23/3/bbac104/6553934?login=false "Click") [**[Code]**](https://github.com/Zj-Teng/DFELMDA "Click")

5. **[MDA-CF]** Dai Q, Chu Y, Li Z et al. MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information, Computers in Biology and Medicine 2021;136:104706. [**[Download]**](https://www.sciencedirect.com/science/article/pii/S001048252100500X "Click") [**[Code]**](https://github.com/a1622108/MDA-CF "Click")

6. **[PMDFI]** Tang M, Liu C, Liu D et al. PMDFI: Predicting miRNA–Disease Associations Based on High-Order Feature Interaction, Frontiers in genetics 2021;12. [**[Download]**](https://www.frontiersin.org/articles/10.3389/fgene.2021.656107/full "Click")

7. **[MSCNE]** Han G, Kuang Z, Deng L. MSCNE:Predict miRNA-Disease Associations Using Neural Network based on Multi-source Biological Information, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2021:1-1. [**[Download]**](https://ieeexplore.ieee.org/document/9519511 "Click")

8. **[SMALF]** Liu D, Huang Y, Nie W et al. SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost, BMC Bioinformatics 2021;22(1):219. [**[Download]**](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04135-2 "Click") [**[Code]**](https://github.com/dayunliu/SMALF "Click")

9. **[MDA-CNN]** Peng J, Hui W, Li Q et al. A learning-based framework for miRNA-disease association identification using neural networks, Bioinformatics 2019;35(21):4364-4371. [**[Download]**](https://academic.oup.com/bioinformatics/article/35/21/4364/5448859?login=false "Click") [**[Code]**](https://github.com/Issingjessica/MDA-CNN "Click")

10. **[AEMDA]** Ji C, Gao Z, Ma X et al. AEMDA: inferring miRNA–disease associations based on deep autoencoder, Bioinformatics 2020;37(1):66-72. [**[Download]**](https://github.com/CunmeiJi/AEMDA "Click") [**[Code]**](https://github.com/CSUBioGroup/SDLDA "Click")

11. **[iMDA-BN]** Zheng K, You Z-H, Wang L et al. iMDA-BN: Identification of miRNA-disease associations based on the biological network and graph embedding algorithm, Computational and Structural Biotechnology Journal 2020;18:2391-2400. [**[Download]**](https://www.sciencedirect.com/science/article/pii/S2001037020303767 "Click")

12. **[DANE-MDA]** 	Ji B-Y, You Z-H, Wang Y et al. DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding, iScience 2021;24(6):102455. [**[Download]**](https://www.sciencedirect.com/science/article/pii/S2589004221004235 "Click") [**[Code]**](https://github.com/jiboya123/DANE-MDA "Click")

13. **[DBNMDA]** Chen X, Li T-H, Zhao Y et al. Deep-belief network for predicting potential miRNA-disease associations, Briefings in Bioinformatics 2020;22(3). [**[Download]**](https://academic.oup.com/bib/article-abstract/22/3/bbaa186/5898648?login=false "Click")

14. **[SAEMD]** Wang C-C, Li T-H, Huang L et al. Prediction of potential miRNA–disease associations based on stacked autoencoder, Briefings in Bioinformatics 2022;23(2). [**[Download]**](https://academic.oup.com/bib/article-abstract/23/2/bbac021/6529883?login=false "Click") [**[Code]**](https://github.com/xpnbs/SAEMDA "Click")

15. **[DBMDA]** Zheng K, You Z-H, Wang L et al. DBMDA: A Unified Embedding for Sequence-Based miRNA Similarity Measure with Applications to Predict and Validate miRNA-Disease Associations, Molecular Therapy - Nucleic Acids 2020;19:602-611. [**[Download]**](https://www.sciencedirect.com/science/article/pii/S2162253119304056 "Click")

16. **[VAEMDA]** Zhang L, Chen X, Yin J. Prediction of Potential miRNA–Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder, Cells 2019;8(9):1040. [**[Download]**](https://www.mdpi.com/2073-4409/8/9/1040 "Click")

17. **[SVAEMDA]** Ji C, Wang Y, Gao Z et al. A Semi-Supervised Learning Method for MiRNA-Disease Association Prediction Based on Variational Autoencoder, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2022;19(4):2049-2059. [**[Download]**](https://ieeexplore.ieee.org/abstract/document/9381658 "Click")

### Multi-layer perceptron
1. **[EPMDA]** Dong Y, Sun Y, Qin C et al. EPMDA: Edge Perturbation Based Method for miRNA-Disease Association Prediction, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020;17(6):2170-2175. [**[Download]**](https://ieeexplore.ieee.org/abstract/document/8827911 "Click")

2. **[MLRDFM]** Ding Y, Lei X, Liao B et al. MLRDFM: a multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations, Briefings in Bioinformatics 2022;23(3). [**[Download]**](https://academic.oup.com/bib/article-abstract/23/3/bbac079/6552270?login=false "Click") [**[Code]**](https://github.com/XYDBCS/MLRDFM "Click")

### Convolutional neural network 
1. **[CNNDMP]** Xuan P, Dong Y, Guo Y et al. Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs, International journal of molecular sciences 2018;19(12):3732. [**[Download]**](https://www.mdpi.com/1422-0067/19/12/3732 "Click")

2. **[CNNMDA]** Xuan P, Sun H, Wang X et al. Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks, International journal of molecular sciences 2019;20(15):3648. [**[Download]**](https://www.mdpi.com/1422-0067/20/15/3648 "Click")

3. **[DNRLCNN]** Zhong J, Zhou W, Kang J et al. DNRLCNN: A CNN Framework for Identifying MiRNA–Disease Associations Using Latent Feature Matrix Extraction with Positive Samples, Interdisciplinary Sciences: Computational Life Sciences 2022;14(2):607-622. [**[Download]**](https://link.springer.com/article/10.1007/s12539-022-00509-z "Click")

## Graph neural models for predicting MDAs 
### graph convolutional network
1. **[HGCNMDA(1)]** Li C, Liu H, Hu Q et al. A Novel Computational Model for Predicting microRNA–Disease Associations Based on Heterogeneous Graph Convolutional Networks, Cells 2019;8(9):977. [**[Download]**](https://www.mdpi.com/2073-4409/8/9/977 "Click")

2. **[Zhu’s method]** Zhu R, Ji C, Wang Y et al. Heterogeneous graph convolutional networks and matrix completion for miRNA-disease association prediction, Frontiers in bioengineering and biotechnology 2020;8:901. [**[Download]**](https://www.frontiersin.org/articles/10.3389/fbioe.2020.00901/full "Click")

3. **[NIMCGCN]** Li J, Zhang S, Liu T et al. Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction, Bioinformatics 2020;36(8):2538-2546. [**[Download]**](https://academic.oup.com/bioinformatics/article/36/8/2538/5697092 "Click") [**[Code]**](https://github.com/ljatynu/NIMCGCN/ "Click")

4. **[FCGCNMDA]** Li J, Li Z, Nie R et al. FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks, Molecular Genetics and Genomics 2020;295(5):1197-1209. [**[Download]**](https://link.springer.com/article/10.1007/s00438-020-01693-7 "Click")

5. **[MMGCN]** Tang X, Luo J, Shen C et al. Multi-view Multichannel Attention Graph Convolutional Network for miRNA–disease association prediction, Briefings in Bioinformatics 2021;22(6). [**[Download]**](https://academic.oup.com/bib/article-abstract/22/6/bbab174/6271996 "Click") [**[Code]**](https://github.com/Txinru/MMGCN "Click")

6. **[GSCENet]** Li Z, Jiang K, Qin S et al. GCSENet: A GCN, CNN and SENet ensemble model for microRNA-disease association prediction, PLOS Computational Biology 2021;17(6):e1009048. [**[Download]**](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009048 "Click") [**[Code]**](https://github.com/Appleabc123/GCSENet "Click")

7. **[MVIFMDA]** Xie X, Wang Y, Sheng N et al. Predicting miRNA-disease associations based on multi-view information fusion, Frontiers in genetics 2022;13. [**[Download]**](https://www.frontiersin.org/articles/10.3389/fgene.2022.979815/full "Click")

8. **[MDA-GCNFTD]** Chu Y, Wang X, Dai Q et al. MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph, Briefings in Bioinformatics 2021;22(6). [**[Download]**](https://academic.oup.com/bib/article-abstract/22/6/bbab165/6261915 "Click") [**[Code]**](https://github.com/a96123155/MDA-GCNFTG "Click")

9. **[MINIMDA]** Lou Z, Cheng Z, Li H et al. Predicting miRNA–disease associations via learning multimodal networks and fusing mixed neighborhood information, Briefings in Bioinformatics 2022. [**[Download]**](https://github.com/chengxu123/MINIMDA "Click") [**[Code]**](https://github.com/Txinru/MMGCN "Click")

10. **[HGCNMDA(2)]** Peng W, Che Z, Dai W et al. Predicting miRNA-disease associations from miRNA-gene-disease heterogeneous network with multi-relational graph convolutional network model, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2022:1-12. [**[Download]**](https://ieeexplore.ieee.org/abstract/document/9813368 "Click") [**[Code]**](https://github.com/weiba/HGCNMDA "Click")

11. **[MuCoMid]** Dong TN, Mucke S, Khosla M. MuCoMiD: A Multitask graph Convolutional Learning Framework for miRNA-Disease Association Prediction, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2022:1-1. [**[Download]**](https://ieeexplore.ieee.org/abstract/document/9779549 "Click") [**[Code]**](https://git.l3s.uni-hannover.de/dong/cmtt "Click")

12. **[SGNNMD]** Zhang G, Li M, Deng H et al. SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations, Briefings in Bioinformatics 2021;23(1). [**[Download]**](https://academic.oup.com/bib/article-abstract/23/1/bbab464/6455665 "Click") [**[Code]**](https://github.com/bubblecode/SGNNMD "Click")

### graph attention network
1. **[HGATMDA]** Ji C, Wang Y, Ni J et al. Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks, Frontiers in genetics 2021;12. [**[Download]**](chrome-extension://cdonnmffkdaoajfknoeeecmchibpmkmg/assets/pdf/web/viewer.html?file=https%3A%2F%2Ffjfsdata01prod.blob.core.windows.net%2Farticles%2Ffiles%2F727744%2Fpubmed-zip%2F.versions%2F1%2F.package-entries%2Ffgene-12-727744%2Ffgene-12-727744.pdf%3Fsv%3D2018-03-28%26sr%3Db%26sig%3DTtrU2rU%252BOHDjdsgM6n2OL4ASfiupSmZRiCzuMkafpWc%253D%26se%3D2022-10-01T08%253A20%253A23Z%26sp%3Dr%26rscd%3Dattachment%253B%2520filename%252A%253DUTF-8%2527%2527fgene-12-727744.pdf "Click")

2. **[GRPAMDA]** Zhong T, Li Z, You Z-H et al. Predicting miRNA–disease associations based on graph random propagation network and attention network, Briefings in Bioinformatics 2022;23(2). [**[Download]**](https://academic.oup.com/bib/article-abstract/23/2/bbab589/6515233 "Click") [**[Code]**](https://github.com/ZTangBo/GRPAMDA "Click")

3. **[HGANMDA]** Li Z, Zhong T, Huang D et al. Hierarchical graph attention network for miRNA-disease association prediction, Molecular Therapy 2022;30(4):1775-1786. [**[Download]**](https://www.sciencedirect.com/science/article/pii/S1525001622000806 "Click") [**[Code]**](https://github.com/ZTangBo/HGANMDA "Click")

4. **[MDPBMP]** Yu L, Zheng Y, Gao L. MiRNA–disease association prediction based on meta-paths, Briefings in Bioinformatics 2022;23(2). [**[Download]**](https://academic.oup.com/bib/article-abstract/23/2/bbab571/6501422 "Click") [**[Code]**](https://github.com/LiangYu-Xidian/MDPBMP "Click")

### graph autoencoder
1. **[GCAEMDA]** Li L, Wang Y-T, Ji C-M et al. GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder, PLOS Computational Biology 2021;17(12):e1009655. [**[Download]**](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009655 "Click") [**[Code]**](https://github.com/zhanglabNKU/VGAELDA "Click")

2. **[NIMGSA]** Jin C, Shi Z, Lin K et al. Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism, Biomolecules 2022;12(1):64. [**[Download]**](https://www.mdpi.com/2218-273X/12/1/64 "Click") [**[Code]**](https://github.com/zhanglabNKU/NIMGSA "Click")

3. **[GAEMDA]** Li Z, Li J, Nie R et al. A graph auto-encoder model for miRNA-disease associations prediction, Briefings in Bioinformatics 2020;22(4). [**[Download]**](https://academic.oup.com/bib/article-abstract/22/4/bbaa240/5929824 "Click") [**[Code]**](https://github.com/chimianbuhetang/GAEMDA "Click")

4. **[AGAEMD]** Zhang H, Fang J, Sun Y et al. Predicting miRNA-disease associations via node-level attention graph auto-encoder, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2022:1-1. [**[Download]**](https://ieeexplore.ieee.org/abstract/document/9767602 "Click") [**[Code]**](https://github.com/Zhhuizhe/AGAEMD "Click")

5. **[VGAE-MDA]** Ding Y, Tian L-P, Lei X et al. Variational graph auto-encoders for miRNA-disease association prediction, Methods 2021;192:25-34. [**[Download]**](https://www.sciencedirect.com/science/article/pii/S104620232030164X "Click")

6. **[VGAMF]** Shi Z, Zhang H, Jin C et al. Ding Y, Lei X, Liao B et al. Predicting miRNA-Disease Associations Based On Multi-View Variational Graph Auto-Encoder With Matrix Factorization, IEEE Journal of Biomedical and Health Informatics 2022;26(1):446-457. [**[Download]**](https://ieeexplore.ieee.org/abstract/document/9451570 "Click") [**[Code]**](https://github.com/XYDBCS/VGAMF "Click")

## Other deep learning methonds
1. **[CEMDA]** Liu B, Zhu X, Zhang L et al. Combined embedding model for MiRNA-disease association prediction, BMC Bioinformatics 2021;22(1):161. [**[Download]**](https://link.springer.com/article/10.1186/s12859-021-04273-7 "Click") [**[Code]**](https://github.com/liubailong/CEMDA "Click")

2. **[GMDA]** Xuan P, Wang D, Cui H et al. Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA–disease association prediction, Briefings in Bioinformatics 2021;23(1). [**[Download]**](https://academic.oup.com/bib/article-abstract/23/1/bbab428/6385813?login=false "Click")

NA denotes a lack of a code.

## Cite
Sheng N, Xie X, Wang Y, et al. A Survey of Deep Learning for Detecting miRNA-Disease Associations: Databases, Computational Methods, Challenges, and Future Directions. IEEE/ACM Transactions on Computational Biology and Bioinformatics, doi: 10.1109/TCBB.2024.3351752, 2024.

## Welcome to contribute
If you would like to help contribute this list, please feel free to contact me by email:

* Email: shengnan21@mails.jlu.edu.cn

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