These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
270 related articles for article (PubMed ID: 33226984)
1. Two-step multi-omics modelling of drug sensitivity in cancer cell lines to identify driving mechanisms. Kusch N; Schuppert A PLoS One; 2020; 15(11):e0238961. PubMed ID: 33226984 [TBL] [Abstract][Full Text] [Related]
2. Gene-centric multi-omics integration with convolutional encoders for cancer drug response prediction. Lee M; Kim PJ; Joe H; Kim HG Comput Biol Med; 2022 Dec; 151(Pt A):106192. PubMed ID: 36327883 [TBL] [Abstract][Full Text] [Related]
3. Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data. Rashid MM; Selvarajoo K Brief Bioinform; 2024 May; 25(4):. PubMed ID: 38904542 [TBL] [Abstract][Full Text] [Related]
4. DROEG: a method for cancer drug response prediction based on omics and essential genes integration. Wu P; Sun R; Fahira A; Chen Y; Jiangzhou H; Wang K; Yang Q; Dai Y; Pan D; Shi Y; Wang Z Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36715269 [TBL] [Abstract][Full Text] [Related]
5. DeepFusionCDR: Employing Multi-Omics Integration and Molecule-Specific Transformers for Enhanced Prediction of Cancer Drug Responses. Hu X; Zhang P; Zhang J; Deng L IEEE J Biomed Health Inform; 2024 Oct; 28(10):6248-6258. PubMed ID: 38935469 [TBL] [Abstract][Full Text] [Related]
6. Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data. Xu Y; Dong Q; Li F; Xu Y; Hu C; Wang J; Shang D; Zheng X; Yang H; Zhang C; Shao M; Meng M; Xiong Z; Li X; Zhang Y J Transl Med; 2019 Aug; 17(1):255. PubMed ID: 31387579 [TBL] [Abstract][Full Text] [Related]
7. Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach. Ali M; Khan SA; Wennerberg K; Aittokallio T Bioinformatics; 2018 Apr; 34(8):1353-1362. PubMed ID: 29186355 [TBL] [Abstract][Full Text] [Related]
8. Deep learning and multi-omics approach to predict drug responses in cancer. Wang C; Lye X; Kaalia R; Kumar P; Rajapakse JC BMC Bioinformatics; 2022 Nov; 22(Suppl 10):632. PubMed ID: 36443676 [TBL] [Abstract][Full Text] [Related]
9. The prediction of drug sensitivity by multi-omics fusion reveals the heterogeneity of drug response in pan-cancer. Wang C; Zhang M; Zhao J; Li B; Xiao X; Zhang Y Comput Biol Med; 2023 Sep; 163():107220. PubMed ID: 37406589 [TBL] [Abstract][Full Text] [Related]
10. Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data. Park S; Soh J; Lee H BMC Bioinformatics; 2021 May; 22(1):269. PubMed ID: 34034645 [TBL] [Abstract][Full Text] [Related]
11. RedCDR: Dual Relation Distillation for Cancer Drug Response Prediction. Xu M; Zhu Z; Zhao Y; He K; Huang Q; Zhao Y IEEE/ACM Trans Comput Biol Bioinform; 2024; 21(5):1468-1479. PubMed ID: 38776197 [TBL] [Abstract][Full Text] [Related]
12. A denoised multi-omics integration framework for cancer subtype classification and survival prediction. Pang J; Liang B; Ding R; Yan Q; Chen R; Xu J Brief Bioinform; 2023 Sep; 24(5):. PubMed ID: 37594302 [TBL] [Abstract][Full Text] [Related]
13. Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction. Liu C; Wang X; Genchev GZ; Lu H Methods; 2017 Jul; 124():100-107. PubMed ID: 28627406 [TBL] [Abstract][Full Text] [Related]
14. Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data. El-Manzalawy Y; Hsieh TY; Shivakumar M; Kim D; Honavar V BMC Med Genomics; 2018 Sep; 11(Suppl 3):71. PubMed ID: 30255801 [TBL] [Abstract][Full Text] [Related]
15. Predicting Drug Response Based on Multi-Omics Fusion and Graph Convolution. Peng W; Chen T; Dai W IEEE J Biomed Health Inform; 2022 Mar; 26(3):1384-1393. PubMed ID: 34347616 [TBL] [Abstract][Full Text] [Related]
16. Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification. Wu D; Wang D; Zhang MQ; Gu J BMC Genomics; 2015 Dec; 16():1022. PubMed ID: 26626453 [TBL] [Abstract][Full Text] [Related]
17. Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets. Wei Z; Zhang Y; Weng W; Chen J; Cai H Brief Bioinform; 2021 May; 22(3):. PubMed ID: 32533167 [TBL] [Abstract][Full Text] [Related]
18. Integrative omics analyses broaden treatment targets in human cancer. Sengupta S; Sun SQ; Huang KL; Oh C; Bailey MH; Varghese R; Wyczalkowski MA; Ning J; Tripathi P; McMichael JF; Johnson KJ; Kandoth C; Welch J; Ma C; Wendl MC; Payne SH; Fenyƶ D; Townsend RR; Dipersio JF; Chen F; Ding L Genome Med; 2018 Jul; 10(1):60. PubMed ID: 30053901 [TBL] [Abstract][Full Text] [Related]
19. Evaluation and comparison of multi-omics data integration methods for cancer subtyping. Duan R; Gao L; Gao Y; Hu Y; Xu H; Huang M; Song K; Wang H; Dong Y; Jiang C; Zhang C; Jia S PLoS Comput Biol; 2021 Aug; 17(8):e1009224. PubMed ID: 34383739 [TBL] [Abstract][Full Text] [Related]
20. [Predicting tumor drug sensitivity with multi-omics data]. Yang C; Liu Z; Dai P; Zhang Y; Huang P; Lin Y; Xie L Sheng Wu Gong Cheng Xue Bao; 2022 Jun; 38(6):2201-2212. PubMed ID: 35786472 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]