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For: 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]
Number Citing Articles
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2 Zadeh Shirazi A, Fornaciari E, McDonnell MD, Yaghoobi M, Cevallos Y, Tello-Oquendo L, Inca D, Gomez GA. The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey. J Pers Med 2020;10:E224. [PMID: 33198332 DOI: 10.3390/jpm10040224] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [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 Li A, Huang HT, Huang HC, Juan HF. LncTx: A network-based method to repurpose drugs acting on the survival-related lncRNAs in lung cancer. Comput Struct Biotechnol J 2021;19:3990-4002. [PMID: 34377365 DOI: 10.1016/j.csbj.2021.07.007] [Reference Citation Analysis]
5 Kourou K, Exarchos KP, Papaloukas C, Sakaloglou P, Exarchos T, Fotiadis DI. Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis. Comput Struct Biotechnol J 2021;19:5546-55. [PMID: 34712399 DOI: 10.1016/j.csbj.2021.10.006] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Firoozbakht F, Yousefi B, Schwikowski B. An overview of machine learning methods for monotherapy drug response prediction. Brief Bioinform 2021:bbab408. [PMID: 34619752 DOI: 10.1093/bib/bbab408] [Reference Citation Analysis]
7 Jha N, Prashar D, Rashid M, Shafiq M, Khan R, Pruncu CI, Tabrez Siddiqui S, Saravana Kumar M. Deep Learning Approach for Discovery of In Silico Drugs for Combating COVID-19. J Healthc Eng 2021;2021:6668985. [PMID: 34326978 DOI: 10.1155/2021/6668985] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
8 O’connor JD, Overton IM, Mcmahon SJ. RadSigBench: a framework for benchmarking functional genomics signatures of cancer cell radiosensitivity. Briefings in Bioinformatics. [DOI: 10.1093/bib/bbab561] [Reference Citation Analysis]
9 Kuenzi BM, Park J, Fong SH, Sanchez KS, Lee J, Kreisberg JF, Ma J, Ideker T. Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells. Cancer Cell 2020;38:672-684.e6. [PMID: 33096023 DOI: 10.1016/j.ccell.2020.09.014] [Cited by in Crossref: 40] [Cited by in F6Publishing: 26] [Article Influence: 20.0] [Reference Citation Analysis]
10 Oh M, Park S, Lee S, Lee D, Lim S, Jeong D, Jo K, Jung I, Kim S. DRIM: A Web-Based System for Investigating Drug Response at the Molecular Level by Condition-Specific Multi-Omics Data Integration. Front Genet 2020;11:564792. [PMID: 33281870 DOI: 10.3389/fgene.2020.564792] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
11 Kong W, Midena G, Chen Y, Athanasiadis P, Wang T, Rousu J, He L, Aittokallio T. Systematic review of computational methods for drug combination prediction. Computational and Structural Biotechnology Journal 2022;20:2807-14. [DOI: 10.1016/j.csbj.2022.05.055] [Reference Citation Analysis]
12 Allegra A, Tonacci A, Sciaccotta R, Genovese S, Musolino C, Pioggia G, Gangemi S. Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection. Cancers (Basel) 2022;14:606. [PMID: 35158874 DOI: 10.3390/cancers14030606] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
13 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]
14 Wu L, Wen Y, Leng D, Zhang Q, Dai C, Wang Z, Liu Z, Yan B, Zhang Y, Wang J, He S, Bo X. Machine learning methods, databases and tools for drug combination prediction. Brief Bioinform 2021:bbab355. [PMID: 34477201 DOI: 10.1093/bib/bbab355] [Reference Citation Analysis]
15 Zhu EY, Dupuy AJ. Machine learning approach informs biology of cancer drug response. BMC Bioinformatics 2022;23:184. [PMID: 35581546 DOI: 10.1186/s12859-022-04720-z] [Reference Citation Analysis]
16 Tong F, Shahid M, Jin P, Jung S, Kim WH, Kim J. Classification of the urinary metabolome using machine learning and potential applications to diagnosing interstitial cystitis. Bladder (San Franc) 2020;7:e43. [PMID: 32775485 DOI: 10.14440/bladder.2020.815] [Reference Citation Analysis]
17 Zuo Z, Wang P, Chen X, Tian L, Ge H, Qian D. SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures. BMC Bioinformatics 2021;22:434. [PMID: 34507532 DOI: 10.1186/s12859-021-04352-9] [Reference Citation Analysis]
18 Carretero-puche C, García-martín S, García-carbonero R, Gómez-lópez G, Al-shahrour F. How can bioinformatics contribute to the routine application of personalized precision medicine? Expert Review of Precision Medicine and Drug Development 2020;5:115-7. [DOI: 10.1080/23808993.2020.1758062] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Prasse P, Iversen P, Lienhard M, Thedinga K, Bauer C, Herwig R, Scheffer T. Matching anticancer compounds and tumor cell lines by neural networks with ranking loss. NAR Genom Bioinform 2022;4:lqab128. [PMID: 35047818 DOI: 10.1093/nargab/lqab128] [Reference Citation Analysis]
20 Koras K, Kizling E, Juraeva D, Staub E, Szczurek E. Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines. Sci Rep 2021;11:15993. [PMID: 34362938 DOI: 10.1038/s41598-021-94564-z] [Reference Citation Analysis]
21 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]
22 Paul D, Abhishek Gupta S, Baijnath Kaushik. DWUT-MLP: Classification of anticancer drug response using various feature selection and classification techniques. Chemometrics and Intelligent Laboratory Systems 2022. [DOI: 10.1016/j.chemolab.2022.104562] [Reference Citation Analysis]
23 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]
24 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]
25 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]
26 Suphavilai C, Chia S, Sharma A, Tu L, Da Silva RP, Mongia A, DasGupta R, Nagarajan N. Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures. Genome Med 2021;13:189. [PMID: 34915921 DOI: 10.1186/s13073-021-01000-y] [Reference Citation Analysis]
27 Kumar R, Dhanda SK. Bird Eye View of Protein Subcellular Localization Prediction. Life (Basel) 2020;10:E347. [PMID: 33327400 DOI: 10.3390/life10120347] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
28 Pratella D, Ait-El-Mkadem Saadi S, Bannwarth S, Paquis-Fluckinger V, Bottini S. A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases. Int J Mol Sci 2021;22:10891. [PMID: 34639231 DOI: 10.3390/ijms221910891] [Reference Citation Analysis]
29 Zhu Y, Brettin T, Evrard YA, Partin A, Xia F, Shukla M, Yoo H, Doroshow JH, Stevens RL. Ensemble transfer learning for the prediction of anti-cancer drug response. Sci Rep 2020;10:18040. [PMID: 33093487 DOI: 10.1038/s41598-020-74921-0] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]