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Sheng N, Huang L, Lu Y et al. Data resources and computational methods for lncRNA-disease association prediction, Computers in Biology and Medicine 2023:106527.

• 20230925

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.

• Overview
• Data resources
  • LncRNA-disease association data resources
  • LncRNA-related data resourcess
  • Disease-related data resources
• Machine learning-based methods
  • Regularized Least Square
  • Support Vector Machine
  • Random Forest
  • Naive Bayesian
• Network propagation-based methods
  • Random walk
  • Flow propagation and label propagation
  • Network inference
  • Other network propagation algorithms
• Matrix factorization- and completion-based methods
  • Matrix factorization
  • Matrix completion
• Deep learning-based methods
  • Full connected neural network
  • Convolutional neural network
  • Autoencoder
  • Generative adversarial network
• Graph neural network-based methods
  • Graph feature extraction
  • Graph matrix completion
• Cancer-related lncRNA candidates
  • Lung cancer-related lncRNA candidates
  • Prostate cancer-related lncRNA candidates
  • Breast cancer-related lncRNA candidates
  • Colorectal cancer-related lncRNA candidates
  • Colon cancer-related lncRNA candidates
  • Cervical cancer-related lncRNA candidates
  • Gastric(Stomach) cancer-related lncRNA candidates
• Cite
• Welcome to contribute

• LncRNA-related and disease-related data resources for LDA prediction are collected and presented, covering LDA data, lncRNA-related data (expression profiles, sequences, genes, miRNAs, proteins), and disease-related data (semantics, phenotypes, genes, miRNAs, proteins).

• We present a timely and comprehensive review of existing computational methods for lncRNA-disease association prediction. Specifically, these methods are systematically classified and divided into 5 categories, including machine learning, network propagation, matrix factorization and completion, deep learning, and graph neural networks, as shown below figure 1. Fig 1: Computational lncRNA-disease association prediction methods based on various models.

• We collected and analyzed potential seven disease-related lncRNAs, which were predicted from LDA computational prediction methods over the last three years (2019-2021), to provide a candidate lncRNAs list for biological experimental verification.

1. [GCLMTP] Sheng, Nan, et al. Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs and diseases. Briefings in Bioinformatics 24.5 (2023): bbad276. [Download] [Code]

2. [LDAGRL] Zhang P, Zhang W, Sun W, et al. A lncRNA-disease association prediction tool development based on bridge heterogeneous information network via graph representation learning for family medicine and primary care[J]. Frontiers in Genetics, 2023, 14: 1084482. [Download])

3. [MCHNLDA] Zhao X, Wu J, Zhao X, et al. Multi-view contrastive heterogeneous graph attention network for lncRNA–disease association prediction[J]. Briefings in Bioinformatics, 2023, 24(1): bbac548. [Download]

4. [LDAEXC] Lu C, Xie M. LDAEXC: LncRNA–Disease Associations Prediction with Deep Autoencoder and XGBoost Classifier[J]. Interdisciplinary Sciences: Computational Life Sciences, 2023: 1-13. [Download]

5. [SSMF-BLNP] Xie G B, Chen R B, Lin Z Y, et al. Predicting lncRNA–disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation[J]. Briefings in Bioinformatics, 2023, 24(1): bbac595. [Download] [Code]

6. [LDAP-WMPS] Wang B, Zhang C, Du X, et al. lncRNA-disease association prediction based on the weight matrix and projection score[J]. Plos one, 2023, 18(1): e0278817. [Download]

7. [LDAF_GAN] Zhong H, Luo J, Tang L, et al. Association filtering and generative adversarial networks for predicting lncRNA-associated disease[J]. BMC bioinformatics, 2023, 24(1): 234. [Download] [Code]

8. [GraLTR-LDA] Liang Q, Zhang W, Wu H, et al. LncRNA-disease association identification using graph auto-encoder and learning to rank[J]. Briefings in Bioinformatics, 2023, 24(1): bbac539. [Download] [Code]

9. [GraLTR-LDA] Zhang Z, Xu J, Wu Y, et al. CapsNet-LDA: predicting lncRNA-disease associations using attention mechanism and capsule network based on multi-view data[J]. Briefings in Bioinformatics, 2023, 24(1): bbac531. [Download] [Code]

1. [LRLSLDA] Chen X, Yan G-Y. Novel human lncRNA–disease association inference based on lncRNA expression profiles, Bioinformatics 2013;29(20):2617-2624. [Download]

2. [LNCSIM] Chen X, Clarence Yan C, Luo C et al. Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity, Scientific Reports 2015;5(1):11338. [Download]

1. [LDAP] Lan W, Li M, Zhao K et al. LDAP: a web server for lncRNA-disease association prediction, Bioinformatics 2016;33(3):458-460. [Download]

2. [ILDMSF] Chen Q, Lai D, Lan W et al. ILDMSF: Inferring Associations Between Long Non-Coding RNA and Disease Based on Multi-Similarity Fusion, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2021;18(3):1106-1112. [Download]

1. [RFLDA] Yao D, Zhan X, Zhan X et al. A random forest based computational model for predicting novel lncRNA-disease associations, BMC Bioinformatics 2020;21(1):126. [Download] [Code]

2. [IPCARF] Zhu R, Wang Y, Liu J-X et al. IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier, BMC Bioinformatics 2021;22(1):175. [Download] [Code]

1. [CFNBC] Yu J, Xuan Z, Feng X et al. A novel collaborative filtering model for LncRNA-disease association prediction based on the Naïve Bayesian classifier, BMC Bioinformatics 2019;20(1):396. [Download] [Code]

2. [NBCLDA] Yu J, Ping P, Wang L et al. A Novel Probability Model for LncRNA–Disease Association Prediction Based on the Naïve Bayesian Classifier, Genes 2018;9(7). [Download]

1. [RWRlncD] Sun J, Shi H, Wang Z et al. Inferring novel lncRNA–disease associations based on a random walk model of a lncRNA functional similarity network, Molecular Biosystems 2014;10(8):2074-2081. [Download]

2. [RWRHLD] Zhou M, Wang X, Li J et al. Prioritizing candidate disease-related long non-coding RNAs by walking on the heterogeneous lncRNA and disease network, Molecular Biosystems 2015;11(3):760-769. [Download]

3. [IRWRLDA] Chen X, You Z-H, Yan G-Y et al. IRWRLDA: improved random walk with restart for lncRNA-disease association prediction, Oncotarget 2016;7(36):57919-57931. [Download]

4. [GrwLDA] Gu C, Liao B, Li X et al. Global network random walk for predicting potential human lncRNA-disease associations, Scientific Reports 2017;7(1):12442. [Download]

5. [BRWLDA] Yu G, Fu G, Lu C et al. BRWLDA: bi-random walks for predicting lncRNA-disease associations, Oncotarget 2017;8(36):60429-60446. [Download]

6. [BiWalkLDA] Hu J, Gao Y, Li J et al. A novel algorithm based on bi-random walks to identify disease-related lncRNAs, BMC Bioinformatics 2019;20(18):569. [Download] [Code]

7. [LDA-LNSUBRW] Xie G, Jiang J, Sun Y. LDA-LNSUBRW: lncRNA-disease association prediction based on linear neighborhood similarity and unbalanced bi-random walk, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2020:1-1. [Download] [Code]

8. [HAUBRW] Xie G, Wu C, Gu G et al. HAUBRW: Hybrid algorithm and unbalanced bi-random walk for predicting lncRNA-disease associations, Genomics 2020;112(6):4777-4787. [Download]

9. [LION] Sumathipala M, Maiorino E, Weiss ST et al. Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION, Frontiers in Physiology 2019;10. [Download]

10. [MHRWR] Zhao X, Yang Y, Yin M. MHRWR: Prediction of lncRNA-Disease Associations Based on Multiple Heterogeneous Networks, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2021;18(6):2577-2585. [Download] [Code]

11. [IDHI-MIRW] Fan X-N, Zhang S-W, Zhang S-Y et al. Prediction of lncRNA-disease associations by integrating diverse heterogeneous information sources with RWR algorithm and positive pointwise mutual information, BMC Bioinformatics 2019;20(1):87. [Download] [Code]

12.[LRWRHLDA] Wang L, Shang M, Dai Q et al. Prediction of lncRNA-disease association based on a Laplace normalized random walk with restart algorithm on heterogeneous networks, BMC Bioinformatics 2022;23(1):5. [Download] [Code]

13. [IIRWR] Wang L, Xiao Y, Li J et al. IIRWR: Internal Inclined Random Walk With Restart for LncRNA-Disease Association Prediction, IEEE Access 2019;7:54034-54041. [Download]

14. [LRWHLDA] Li J, Zhao H, Xuan Z et al. A Novel Approach for Potential Human LncRNA-Disease Association Prediction Based on Local Random Walk, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2021;18(3):1049-1059. [Download]

1. [LncRDNetFlow] Zhang J, Zhang Z, Chen Z et al. Integrating Multiple Heterogeneous Networks for Novel LncRNA-Disease Association Inference, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2019;16(2):396-406. [Download]

2. [LLCLPLDA] Xie G, Huang S, Luo Y et al. LLCLPLDA: a novel model for predicting lncRNA–disease associations, Molecular Genetics and Genomics 2019;294(6):1477-1486. [Download]

1. [Ping et al] Ping P, Wang L, Kuang L et al. A Novel Method for LncRNA-Disease Association Prediction Based on an lncRNA-Disease Association Network, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2019;16(2):688-693. [Download]

1. [Yang et al] Yang X, Gao L, Guo X et al. A Network Based Method for Analysis of lncRNA-Disease Associations and Prediction of lncRNAs Implicated in Diseases, PLOS ONE 2014;9(1):e87797. [Download]

2. [TPGLDA] Ding L, Wang M, Sun D et al. TPGLDA: Novel prediction of associations between lncRNAs and diseases via lncRNA-disease-gene tripartite graph, Scientific Reports 2018;8(1):1065. [Download] [Code]

3. [IDLDA] Wang Q, Yan G. IDLDA: An Improved Diffusion Model for Predicting LncRNA–Disease Associations, Frontiers in genetics 2019;10. [Download]

4. [NCPLDA] Li G, Luo J, Liang C et al. Prediction of LncRNA-Disease Associations Based on Network Consistency Projection, IEEE Access 2019;7:58849-58856. [Download] [Code]

5. [LDAI-ISPS] Zhang Y, Chen M, Li A et al. LDAI-ISPS: LncRNA–Disease Associations Inference Based on Integrated Space Projection Scores, International journal of molecular sciences 2020;21(4). [Download]

6. [LDAH2V] Deng L, Li W, Zhang J. LDAH2V: Exploring Meta-Paths Across Multiple Networks for lncRNA-Disease Association Prediction, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2021;18(4):1572-1581. [Download]

7. [SVDNVLDA] Li J, Li J, Kong M et al. SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec, BMC Bioinformatics 2021;22(1):538. [Download] [Code]

1. [MFLDA] Fu G, Wang J, Domeniconi C et al. Matrix factorization-based data fusion for the prediction of lncRNA–disease associations, Bioinformatics 2017;34(9):1529-1537. [Download] [Code]

2. [WMFLDA] Wang Y, Yu G, Wang J et al. Weighted matrix factorization on multi-relational data for LncRNA-disease association prediction, Methods 2020;173:32-43. [Download] [Code]

3. [DNILMF-LDA ] Li Y, Li J, Bian N. DNILMF-LDA: Prediction of lncRNA-Disease Associations by Dual-Network Integrated Logistic Matrix Factorization and Bayesian Optimization, Genes 2019;10(8). [Download]

4. [PMFILDA] Xuan Z, Li J, Yu J et al. A Probabilistic Matrix Factorization Method for Identifying lncRNA-Disease Associations, Genes 2019;10(2). [Download]

5. [WGRCMF] Liu JX, Cui Z, Gao YL et al. WGRCMF: A Weighted Graph Regularized Collaborative Matrix Factorization Method for Predicting Novel LncRNA-Disease Associations, IEEE Journal of Biomedical and Health Informatics 2021;25(1):257-265. [Download]

6. [LDGRNMF] Wang M-N, You Z-H, Wang L et al. LDGRNMF: LncRNA-disease associations prediction based on graph regularized non-negative matrix factorization, Neurocomputing 2021;424:236-245. [Download]

1. [SIMCLDA] Lu C, Yang M, Luo F et al. Prediction of lncRNA–disease associations based on inductive matrix completion, Bioinformatics 2018;34(19):3357-3364. [Download] [Code]

2. [GMCLDA] Lu C, Yang M, Li M et al. Predicting Human lncRNA-Disease Associations Based on Geometric Matrix Completion, IEEE Journal of Biomedical and Health Informatics 2020;24(8):2420-2429. [Download] [Code]

1. [NNLDA] Hu J, Gao Y, Li J et al. Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease, Frontiers in genetics 2019;10. [Download] [Code]

2. [DMFLDA] Zeng M, Lu C, Fei Z et al. DMFLDA: A Deep Learning Framework for Predicting lncRNA–Disease Associations, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2021;18(6):2353-2363. [Download] [Code]

3. [SDLDA] Zeng M, Lu C, Zhang F et al. SDLDA: lncRNA-disease association prediction based on singular value decomposition and deep learning, Methods 2020;179:73-80. [Download] [Code]

1. [CNNLDA] Xuan P, Cao Y, Zhang T et al. Dual Convolutional Neural Networks With Attention Mechanisms Based Method for Predicting Disease-Related lncRNA Genes, Frontiers in genetics 2019;10. [Download]

2. [LDApred] Xuan P, Jia L, Zhang T et al. LDAPred: A Method Based on Information Flow Propagation and a Convolutional Neural Network for the Prediction of Disease-Associated lncRNAs, International journal of molecular sciences 2019;20(18). [Download]

3. [iLncRNAdis-FB] Wei H, Liao Q, Liu B. iLncRNAdis-FB: Identify lncRNA-Disease Associations by Fusing Biological Feature Blocks Through Deep Neural Network, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2021;18(5):1946-1957. [Download]

4. [MCA-Net] Zhang Y, Ye F, Gao X. MCA-Net: Multi-feature coding and attention convolutional neural network for predicting lncRNA-disease association, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2021:1-1. [Download]

1. [CNNDLP] Xuan P, Sheng N, Zhang T et al. CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA–Disease Associations, International journal of molecular sciences 2019;20(17). [Download]

2. [LDNFSGB] Zhang Y, Ye F, Xiong D et al. LDNFSGB: prediction of long non-coding rna and disease association using network feature similarity and gradient boosting, BMC Bioinformatics 2020;21(1):377. [Download] [Code]

3. [LDASR] Guo Z-H, You Z-H, Wang Y-B et al. A Learning-Based Method for LncRNA-Disease Association Identification Combing Similarity Information and Rotation Forest, iScience 2019;19:786-795. [Download]

4. [MAN] Su X, You Z, Yi H. Prediction of LncRNA-Disease Associations Based on Network Representation Learning. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2020, p.1805-1812. [Download]

5. [VADLP] Sheng N, Cui H, Zhang T et al. Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA–disease association prediction, Briefings in Bioinformatics 2020;22(3). [Download]

1. [BiGAN] Yang Q, Li X. BiGAN: LncRNA-disease association prediction based on bidirectional generative adversarial network, BMC Bioinformatics 2021;22(1):357. [Download] [Code]

1. [GCNLDA] Xuan P, Pan S, Zhang T et al. Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations, Cells 2019;8(9). [Download]

2. [GAERF] Wu Q-W, Xia J-F, Ni J-C et al. GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest, Briefings in Bioinformatics 2021;22(5). [Download]

3. [MLGCNET] Wu QW, Cao RF, Xia J et al. Extra Trees Method for Predicting LncRNA-Disease Association Based on Multi-layer Graph Embedding Aggregation, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2021:1-1. [Download] [Code]

4. [MGATE] Sheng N, Huang L, Wang Y et al. Sheng N, Huang L, Wang Y et al. Multi-channel graph attention autoencoders for disease-related lncRNAs prediction, Briefings in Bioinformatics 2022;23(2). [Download] [Code]

5. [GANLDA] Lan W, Wu X, Chen Q et al. GANLDA: Graph attention network for lncRNA-disease associations prediction, Neurocomputing 2022;469:384-393. [Download]

6. [GTAN] Xuan P, Zhan L, Cui H et al. Graph Triple-Attention Network for Disease-related LncRNA Prediction, IEEE Journal of Biomedical and Health Informatics 2021:1-1. [Download]

7. [PANDA] Silva ABOV, Spinosa EJ. Graph Convolutional Auto-Encoders for predicting novel lncRNA-Disease associations, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2021:1-1. [Download]

1. [GAMCLDA] Wu X, Lan W, Chen Q et al. Inferring LncRNA-disease associations based on graph autoencoder matrix completion, Computational Biology and Chemistry 2020;87:107282. [Download]

2. [GCRFLDA] Fan Y, Chen M, Pan X. GCRFLDA: scoring lncRNA-disease associations using graph convolution matrix completion with conditional random field, Briefings in Bioinformatics 2021;23(1). [Download] [Code]

3. [HGATLDA] Zhao X, Zhao X, Yin M. Heterogeneous graph attention network based on meta-paths for lncRNA–disease association prediction, Briefings in Bioinformatics 2021;23(1). [Download]

4. [VGAELDA] Shi Z, Zhang H, Jin C et al. A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations, BMC Bioinformatics 2021;22(1):136. [Download] [Code]

Sheng N, Huang L, Lu Y et al. Data resources and computational methods for lncRNA-disease association prediction, Computers in Biology and Medicine 2023:106527.

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• Email: [email protected]