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Cited by in F6Publishing
For: Liang X, Zhang P, Yan L, Fu Y, Peng F, Qu L, Shao M, Chen Y, Chen Z. LRSSL: predict and interpret drug–disease associations based on data integration using sparse subspace learning. Bioinformatics. [DOI: 10.1093/bioinformatics/btw770] [Cited by in Crossref: 23] [Cited by in F6Publishing: 26] [Article Influence: 4.6] [Reference Citation Analysis]
Number Citing Articles
1 Xuan P, Cui H, Shen T, Sheng N, Zhang T. HeteroDualNet: A Dual Convolutional Neural Network With Heterogeneous Layers for Drug-Disease Association Prediction via Chou's Five-Step Rule. Front Pharmacol 2019;10:1301. [PMID: 31780934 DOI: 10.3389/fphar.2019.01301] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 1.3] [Reference Citation Analysis]
2 Hu P, Huang YA, Mei J, Leung H, Chen ZH, Kuang ZM, You ZH, Hu L. Learning from low-rank multimodal representations for predicting disease-drug associations. BMC Med Inform Decis Mak 2021;21:308. [PMID: 34736437 DOI: 10.1186/s12911-021-01648-x] [Reference Citation Analysis]
3 Wang J, Wang W, Yan C, Luo J, Zhang G. Predicting Drug-Disease Association Based on Ensemble Strategy. Front Genet 2021;12:666575. [PMID: 34012464 DOI: 10.3389/fgene.2021.666575] [Reference Citation Analysis]
4 Xuan P, Song Y, Zhang T, Jia L. Prediction of Potential Drug-Disease Associations through Deep Integration of Diversity and Projections of Various Drug Features. Int J Mol Sci 2019;20:E4102. [PMID: 31443472 DOI: 10.3390/ijms20174102] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
5 Shi W, Chen X, Deng L. A Review of Recent Developments and Progress in Computational Drug Repositioning. Curr Pharm Des 2020;26:3059-68. [PMID: 31951162 DOI: 10.2174/1381612826666200116145559] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
6 Li Z, Huang Q, Chen X, Wang Y, Li J, Xie Y, Dai Z, Zou X. Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network. Front Chem 2019;7:924. [PMID: 31998700 DOI: 10.3389/fchem.2019.00924] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
7 Wu G, Liu J, Wang C. Predicting drug-disease interactions by semi-supervised graph cut algorithm and three-layer data integration. BMC Med Genomics 2017;10:79. [PMID: 29297383 DOI: 10.1186/s12920-017-0311-0] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 2.2] [Reference Citation Analysis]
8 Jiang HJ, You ZH, Huang YA. Predicting drug-disease associations via sigmoid kernel-based convolutional neural networks. J Transl Med 2019;17:382. [PMID: 31747915 DOI: 10.1186/s12967-019-2127-5] [Cited by in Crossref: 7] [Cited by in F6Publishing: 1] [Article Influence: 2.3] [Reference Citation Analysis]
9 Ma Y, Li Q, Hu N, Li L. SeBioGraph: Semi-supervised Deep Learning for the Graph via Sustainable Knowledge Transfer. Front Neurorobot 2021;15:665055. [PMID: 33867966 DOI: 10.3389/fnbot.2021.665055] [Reference Citation Analysis]
10 Zheng S, Ma H, Wang J, Li J. A Computational Bipartite Graph-Based Drug Repurposing Method. Methods Mol Biol 2019;1903:115-27. [PMID: 30547439 DOI: 10.1007/978-1-4939-8955-3_7] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
11 Li J, Zhang S, Wan Y, Zhao Y, Shi J, Zhou Y, Cui Q. MISIM v2.0: a web server for inferring microRNA functional similarity based on microRNA-disease associations. Nucleic Acids Res 2019;47:W536-41. [PMID: 31069374 DOI: 10.1093/nar/gkz328] [Cited by in Crossref: 15] [Cited by in F6Publishing: 13] [Article Influence: 7.5] [Reference Citation Analysis]
12 Chao CT, Tsai YT, Lee WT, Yeh HY, Chiang CK. Deep Learning-Assisted Repurposing of Plant Compounds for Treating Vascular Calcification: An In Silico Study with Experimental Validation. Oxid Med Cell Longev 2022;2022:4378413. [PMID: 35035662 DOI: 10.1155/2022/4378413] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Jiang HJ, Huang YA, You ZH. Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder. Biomed Res Int 2019;2019:2426958. [PMID: 31534955 DOI: 10.1155/2019/2426958] [Cited by in Crossref: 10] [Cited by in F6Publishing: 4] [Article Influence: 3.3] [Reference Citation Analysis]
14 Jiang HJ, Huang YA, You ZH. SAEROF: an ensemble approach for large-scale drug-disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network. Sci Rep. 2020;10:4972. [PMID: 32188871 DOI: 10.1038/s41598-020-61616-9] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
15 Yue X, Wang Z, Huang J, Parthasarathy S, Moosavinasab S, Huang Y, Lin SM, Zhang W, Zhang P, Sun H. Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 2020;36:1241-51. [PMID: 31584634 DOI: 10.1093/bioinformatics/btz718] [Cited by in Crossref: 21] [Cited by in F6Publishing: 27] [Article Influence: 10.5] [Reference Citation Analysis]
16 Wu G, Liu J, Yue X. Prediction of drug-disease associations based on ensemble meta paths and singular value decomposition. BMC Bioinformatics 2019;20:134. [PMID: 30925858 DOI: 10.1186/s12859-019-2644-5] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 2.7] [Reference Citation Analysis]
17 Liang X, Zhang P, Li J, Fu Y, Qu L, Chen Y, Chen Z. Learning important features from multi-view data to predict drug side effects. J Cheminform 2019;11:79. [PMID: 33430979 DOI: 10.1186/s13321-019-0402-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
18 Zhang W, Yue X, Lin W, Wu W, Liu R, Huang F, Liu F. Predicting drug-disease associations by using similarity constrained matrix factorization. BMC Bioinformatics 2018;19:233. [PMID: 29914348 DOI: 10.1186/s12859-018-2220-4] [Cited by in Crossref: 68] [Cited by in F6Publishing: 51] [Article Influence: 17.0] [Reference Citation Analysis]
19 Jarada TN, Rokne JG, Alhajj R. SNF-NN: computational method to predict drug-disease interactions using similarity network fusion and neural networks. BMC Bioinformatics 2021;22:28. [PMID: 33482713 DOI: 10.1186/s12859-020-03950-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
20 Huang F, Qiu Y, Li Q, Liu S, Ni F. Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization. Front Bioeng Biotechnol 2020;8:218. [PMID: 32373595 DOI: 10.3389/fbioe.2020.00218] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
21 Xuan P, Ye Y, Zhang T, Zhao L, Sun C. Convolutional Neural Network and Bidirectional Long Short-Term Memory-Based Method for Predicting Drug-Disease Associations. Cells 2019;8:E705. [PMID: 31336774 DOI: 10.3390/cells8070705] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 2.3] [Reference Citation Analysis]
22 Ge S, Wang X, Cheng Y, Liu J. Cancer Subtype Recognition Based on Laplacian Rank Constrained Multiview Clustering. Genes (Basel) 2021;12:526. [PMID: 33916856 DOI: 10.3390/genes12040526] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Xuan P, Zhao L, Zhang T, Ye Y, Zhang Y. Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit. Molecules 2019;24:E2712. [PMID: 31349692 DOI: 10.3390/molecules24152712] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 1.3] [Reference Citation Analysis]
24 Yu SP, Liang C, Xiao Q, Li GH, Ding PJ, Luo JW. MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation. J Cell Mol Med 2019;23:1427-38. [PMID: 30499204 DOI: 10.1111/jcmm.14048] [Cited by in Crossref: 19] [Cited by in F6Publishing: 23] [Article Influence: 4.8] [Reference Citation Analysis]
25 Chen X, Huang L. LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction. PLoS Comput Biol 2017;13:e1005912. [PMID: 29253885 DOI: 10.1371/journal.pcbi.1005912] [Cited by in Crossref: 157] [Cited by in F6Publishing: 142] [Article Influence: 31.4] [Reference Citation Analysis]
26 Zhao BW, You ZH, Wong L, Zhang P, Li HY, Wang L. MGRL: Predicting Drug-Disease Associations Based on Multi-Graph Representation Learning. Front Genet 2021;12:657182. [PMID: 34054920 DOI: 10.3389/fgene.2021.657182] [Reference Citation Analysis]
27 Tian Z, Teng Z, Cheng S, Guo M. Computational drug repositioning using meta-path-based semantic network analysis. BMC Syst Biol 2018;12:134. [PMID: 30598084 DOI: 10.1186/s12918-018-0658-7] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 1.5] [Reference Citation Analysis]
28 Wang X, Yan R. DDAPRED: a computational method for predicting drug repositioning using regularized logistic matrix factorization. J Mol Model 2020;26:60. [PMID: 32062701 DOI: 10.1007/s00894-020-4315-x] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]