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For: 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]
Number Citing Articles
1 Julkunen H, Cichonska A, Gautam P, Szedmak S, Douat J, Pahikkala T, Aittokallio T, Rousu J. Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects. Nat Commun 2020;11:6136. [PMID: 33262326 DOI: 10.1038/s41467-020-19950-z] [Cited by in Crossref: 14] [Cited by in F6Publishing: 9] [Article Influence: 7.0] [Reference Citation Analysis]
2 Maeser D, Gruener RF, Huang RS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform 2021:bbab260. [PMID: 34260682 DOI: 10.1093/bib/bbab260] [Reference Citation Analysis]
3 Güvenç Paltun B, Kaski S, Mamitsuka H. Machine learning approaches for drug combination therapies. Brief Bioinform 2021:bbab293. [PMID: 34368832 DOI: 10.1093/bib/bbab293] [Reference Citation Analysis]
4 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]
5 Li Y, Umbach DM, Krahn JM, Shats I, Li X, Li L. Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines. BMC Genomics 2021;22:272. [PMID: 33858332 DOI: 10.1186/s12864-021-07581-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
6 Sharifi-Noghabi H, Peng S, Zolotareva O, Collins CC, Ester M. AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics. Bioinformatics 2020;36:i380-8. [PMID: 32657371 DOI: 10.1093/bioinformatics/btaa442] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 8.0] [Reference Citation Analysis]
7 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]
8 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]
9 Mourragui SMC, Loog M, Vis DJ, Moore K, Manjon AG, van de Wiel MA, Reinders MJT, Wessels LFA. Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning. Proc Natl Acad Sci U S A 2021;118:e2106682118. [PMID: 34873056 DOI: 10.1073/pnas.2106682118] [Reference Citation Analysis]
10 Zagidullin B, Wang Z, Guan Y, Pitkänen E, Tang J. Comparative analysis of molecular fingerprints in prediction of drug combination effects. Brief Bioinform 2021;22:bbab291. [PMID: 34401895 DOI: 10.1093/bib/bbab291] [Reference Citation Analysis]
11 Demirel HC, Arici MK, Tuncbag N. Computational approaches leveraging integrated connections of multi-omic data toward clinical applications. Mol Omics 2021. [PMID: 34734935 DOI: 10.1039/d1mo00158b] [Reference Citation Analysis]
12 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]