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For: Liu P, Li H, Li S, Leung KS. Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network. BMC Bioinformatics 2019;20:408. [PMID: 31357929 DOI: 10.1186/s12859-019-2910-6] [Cited by in Crossref: 24] [Cited by in F6Publishing: 22] [Article Influence: 8.0] [Reference Citation Analysis]
Number Citing Articles
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2 Rafique R, Islam SMR, Kazi JU. Machine learning in the prediction of cancer therapy. Comput Struct Biotechnol J 2021;19:4003-17. [PMID: 34377366 DOI: 10.1016/j.csbj.2021.07.003] [Reference Citation Analysis]
3 Feng F, Shen B, Mou X, Li Y, Li H. Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine. J Genet Genomics 2021:S1673-8527(21)00084-9. [PMID: 34023295 DOI: 10.1016/j.jgg.2021.03.007] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
4 Güvenç Paltun B, Mamitsuka H, Kaski S. Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches. Brief Bioinform 2021;22:346-59. [PMID: 31838491 DOI: 10.1093/bib/bbz153] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 3.0] [Reference Citation Analysis]
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7 Matsuzaka Y, Uesawa Y. DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance. Front Bioeng Biotechnol 2019;7:485. [PMID: 32039185 DOI: 10.3389/fbioe.2019.00485] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 3.5] [Reference Citation Analysis]
8 Lee Y, Nam S. Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction. Int J Mol Sci 2021;22:7721. [PMID: 34299341 DOI: 10.3390/ijms22147721] [Reference Citation Analysis]
9 Ma T, Liu Q, Li H, Zhou M, Jiang R, Zhang X. DualGCN: a dual graph convolutional network model to predict cancer drug response. BMC Bioinformatics 2022;23. [DOI: 10.1186/s12859-022-04664-4] [Reference Citation Analysis]
10 Wang Y, Yang Y, Chen S, Wang J. DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration. Brief Bioinform 2021:bbab048. [PMID: 33822890 DOI: 10.1093/bib/bbab048] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
11 Sarno F, Benincasa G, List M, Barabasi AL, Baumbach J, Ciardiello F, Filetti S, Glass K, Loscalzo J, Marchese C, Maron BA, Paci P, Parini P, Petrillo E, Silverman EK, Verrienti A, Altucci L, Napoli C; International Network Medicine Consortium. Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside. Clin Epigenetics 2021;13:66. [PMID: 33785068 DOI: 10.1186/s13148-021-01047-z] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
12 Tan X, Yu Y, Duan K, Zhang J, Sun P, Sun H. Current Advances and Limitations of Deep Learning in Anticancer Drug Sensitivity Prediction. Curr Top Med Chem 2020;20:1858-67. [PMID: 32648840 DOI: 10.2174/1568026620666200710101307] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
13 Huang LC, Yeung W, Wang Y, Cheng H, Venkat A, Li S, Ma P, Rasheed K, Kannan N. Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction. BMC Bioinformatics 2020;21:520. [PMID: 33183223 DOI: 10.1186/s12859-020-03842-6] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
14 Wei Q, Ramsey SA. Predicting chemotherapy response using a variational autoencoder approach. BMC Bioinformatics 2021;22:453. [PMID: 34551729 DOI: 10.1186/s12859-021-04339-6] [Reference Citation Analysis]
15 Baptista D, Ferreira PG, Rocha M. Deep learning for drug response prediction in cancer. Brief Bioinform 2021;22:360-79. [PMID: 31950132 DOI: 10.1093/bib/bbz171] [Cited by in Crossref: 15] [Cited by in F6Publishing: 18] [Article Influence: 7.5] [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 Bazgir O, Ghosh S, Pal R. Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction. Bioinformatics 2021;37:i42-50. [PMID: 34252971 DOI: 10.1093/bioinformatics/btab336] [Reference Citation Analysis]
18 Saber-Ayad M, Hammoudeh S, Abu-Gharbieh E, Hamoudi R, Tarazi H, Al-Tel TH, Hamid Q. Current Status of Baricitinib as a Repurposed Therapy for COVID-19. Pharmaceuticals (Basel) 2021;14:680. [PMID: 34358107 DOI: 10.3390/ph14070680] [Reference Citation Analysis]
19 Sakai M, Nagayasu K, Shibui N, Andoh C, Takayama K, Shirakawa H, Kaneko S. Prediction of pharmacological activities from chemical structures with graph convolutional neural networks. Sci Rep 2021;11:525. [PMID: 33436854 DOI: 10.1038/s41598-020-80113-7] [Reference Citation Analysis]
20 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]
21 Xia F, Allen J, Balaprakash P, Brettin T, Garcia-Cardona C, Clyde A, Cohn J, Doroshow J, Duan X, Dubinkina V, Evrard Y, Fan YJ, Gans J, He S, Lu P, Maslov S, Partin A, Shukla M, Stahlberg E, Wozniak JM, Yoo H, Zaki G, Zhu Y, Stevens R. A cross-study analysis of drug response prediction in cancer cell lines. Brief Bioinform 2021:bbab356. [PMID: 34524425 DOI: 10.1093/bib/bbab356] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 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]
23 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]
24 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]
25 Auslander N, Gussow AB, Koonin EV. Incorporating Machine Learning into Established Bioinformatics Frameworks. Int J Mol Sci 2021;22:2903. [PMID: 33809353 DOI: 10.3390/ijms22062903] [Reference Citation Analysis]
26 Pei Z, Shi M, Guo J, Shen B. Heart Rate Variability Based Prediction of Personalized Drug Therapeutic Response: The Present Status and the Perspectives. CTMC 2020;20:1640-50. [DOI: 10.2174/1568026620666200603105002] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
27 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]
28 Smaïl-Tabbone M, Rance B; Section Editors for the IMIA Yearbook Section on Bioinformatics and Translational Informatics. Contributions from the 2019 Literature on Bioinformatics and Translational Informatics. Yearb Med Inform 2020;29:188-92. [PMID: 32823315 DOI: 10.1055/s-0040-1702002] [Cited by in F6Publishing: 1] [Reference Citation Analysis]