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For: Liu Q, Hu Z, Jiang R, Zhou M. DeepCDR: a hybrid graph convolutional network for predicting cancer drug response. Bioinformatics 2020;36:i911-8. [PMID: 33381841 DOI: 10.1093/bioinformatics/btaa822] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 6.0] [Reference Citation Analysis]
Number Citing Articles
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2 Wang H, Wang Z, Chen J, Liu W. Graph Attention Network Model with Defined Applicability Domains for Screening PBT Chemicals. Environ Sci Technol 2022;56:6774-85. [PMID: 35475611 DOI: 10.1021/acs.est.2c00765] [Reference Citation Analysis]
3 Jiang L, Jiang C, Yu X, Fu R, Jin S, Liu X. DeepTTA: a transformer-based model for predicting cancer drug response. Brief Bioinform 2022:bbac100. [PMID: 35348595 DOI: 10.1093/bib/bbac100] [Reference Citation Analysis]
4 Li X, Ma J, Leng L, Han M, Li M, He F, Zhu Y. MoGCN: A Multi-Omics Integration Method Based on Graph Convolutional Network for Cancer Subtype Analysis. Front Genet 2022;13:806842. [PMID: 35186034 DOI: 10.3389/fgene.2022.806842] [Reference Citation Analysis]
5 Picard M, Scott-Boyer MP, Bodein A, Périn O, Droit A. Integration strategies of multi-omics data for machine learning analysis. Comput Struct Biotechnol J 2021;19:3735-46. [PMID: 34285775 DOI: 10.1016/j.csbj.2021.06.030] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
6 Liu X, Song C, Huang F, Fu H, Xiao W, Zhang W. GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction. Brief Bioinform 2021:bbab457. [PMID: 34727569 DOI: 10.1093/bib/bbab457] [Reference Citation Analysis]
7 Zhang P, Wei Z, Che C, Jin B. DeepMGT-DTI: Transformer network incorporating multilayer graph information for Drug-Target interaction prediction. Comput Biol Med 2022;142:105214. [PMID: 35030496 DOI: 10.1016/j.compbiomed.2022.105214] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
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10 Khan D, Shedole S. Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer. JPM 2022;12:674. [DOI: 10.3390/jpm12050674] [Reference Citation Analysis]
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13 Yang X, Wang W, Ma JL, Qiu YL, Lu K, Cao DS, Wu CK. BioNet: a large-scale and heterogeneous biological network model for interaction prediction with graph convolution. Brief Bioinform 2021:bbab491. [PMID: 34849567 DOI: 10.1093/bib/bbab491] [Reference Citation Analysis]
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15 Gu Y, Zheng S, Xu Z, Yin Q, Li L, Li J. An efficient curriculum learning-based strategy for molecular graph learning. Briefings in Bioinformatics. [DOI: 10.1093/bib/bbac099] [Reference Citation Analysis]
16 An X, Chen X, Yi D, Li H, Guan Y. Representation of molecules for drug response prediction. Brief Bioinform 2021:bbab393. [PMID: 34571534 DOI: 10.1093/bib/bbab393] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Chen Y, Zhang L. How much can deep learning improve prediction of the responses to drugs in cancer cell lines? Brief Bioinform 2021:bbab378. [PMID: 34529029 DOI: 10.1093/bib/bbab378] [Reference Citation Analysis]
18 Wang Z, Wang Z, Huang Y, Lu L, Fu Y. A multi-view multi-omics model for cancer drug response prediction. Appl Intell. [DOI: 10.1007/s10489-022-03294-w] [Reference Citation Analysis]
19 Zhu Y, Ouyang Z, Chen W, Feng R, Chen DZ, Cao J, Wu J. TGSA: Protein-Protein Association-Based Twin Graph Neural Networks for Drug Response Prediction with Similarity Augmentation. Bioinformatics 2021:btab650. [PMID: 34559177 DOI: 10.1093/bioinformatics/btab650] [Reference Citation Analysis]
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