169 related articles for article (PubMed ID: 37370938)
1. NeuPD-A Neural Network-Based Approach to Predict Antineoplastic Drug Response.
Shahzad M; Tahir MA; Alhussein M; Mobin A; Shams Malick RA; Anwar MS
Diagnostics (Basel); 2023 Jun; 13(12):. PubMed ID: 37370938
[TBL] [Abstract][Full Text] [Related]
2. DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines.
Li M; Wang Y; Zheng R; Shi X; Li Y; Wu FX; Wang J
IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(2):575-582. PubMed ID: 31150344
[TBL] [Abstract][Full Text] [Related]
3. Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions.
Peng W; Liu H; Dai W; Yu N; Wang J
Bioinformatics; 2022 Sep; 38(19):4546-4553. PubMed ID: 35997568
[TBL] [Abstract][Full Text] [Related]
4. Predicting breast cancer drug response using a multiple-layer cell line drug response network model.
Huang S; Hu P; Lakowski TM
BMC Cancer; 2021 May; 21(1):648. PubMed ID: 34059012
[TBL] [Abstract][Full Text] [Related]
5. Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization.
Guan NN; Zhao Y; Wang CC; Li JQ; Chen X; Piao X
Mol Ther Nucleic Acids; 2019 Sep; 17():164-174. PubMed ID: 31265947
[TBL] [Abstract][Full Text] [Related]
6. Gamma distribution based predicting model for breast cancer drug response based on multi-layer feature selection.
Cui T; Wang Z; Gu H; Qin P; Wang J
Front Genet; 2023; 14():1095976. PubMed ID: 36816042
[TBL] [Abstract][Full Text] [Related]
7. Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization.
Wang L; Li X; Zhang L; Gao Q
BMC Cancer; 2017 Aug; 17(1):513. PubMed ID: 28768489
[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. Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network.
Liu P; Li H; Li S; Leung KS
BMC Bioinformatics; 2019 Jul; 20(1):408. PubMed ID: 31357929
[TBL] [Abstract][Full Text] [Related]
10. Predicting drug response of tumors from integrated genomic profiles by deep neural networks.
Chiu YC; Chen HH; Zhang T; Zhang S; Gorthi A; Wang LJ; Huang Y; Chen Y
BMC Med Genomics; 2019 Jan; 12(Suppl 1):18. PubMed ID: 30704458
[TBL] [Abstract][Full Text] [Related]
11. An Improved Anticancer Drug-Response Prediction Based on an Ensemble Method Integrating Matrix Completion and Ridge Regression.
Liu C; Wei D; Xiang J; Ren F; Huang L; Lang J; Tian G; Li Y; Yang J
Mol Ther Nucleic Acids; 2020 Sep; 21():676-686. PubMed ID: 32759058
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties.
Menden MP; Iorio F; Garnett M; McDermott U; Benes CH; Ballester PJ; Saez-Rodriguez J
PLoS One; 2013; 8(4):e61318. PubMed ID: 23646105
[TBL] [Abstract][Full Text] [Related]
14. Clinical drug response prediction from preclinical cancer cell lines by logistic matrix factorization approach.
Emdadi A; Eslahchi C
J Bioinform Comput Biol; 2022 Apr; 20(2):2150035. PubMed ID: 34923927
[TBL] [Abstract][Full Text] [Related]
15. [Prediction of drug-induced cell viability by SAE-XGBoost algorithm based on LINCS-L1000 perturbation signal].
Lu J; Chen M; Qin Y; Yu X
Sheng Wu Gong Cheng Xue Bao; 2021 Apr; 37(4):1346-1359. PubMed ID: 33973447
[TBL] [Abstract][Full Text] [Related]
16. Improving drug response prediction based on two-space graph convolution.
Peng W; Chen T; Liu H; Dai W; Yu N; Lan W
Comput Biol Med; 2023 May; 158():106859. PubMed ID: 37023539
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Ensembled machine learning framework for drug sensitivity prediction.
Sharma A; Rani R
IET Syst Biol; 2020 Feb; 14(1):39-46. PubMed ID: 31931480
[TBL] [Abstract][Full Text] [Related]
19. kESVR: An Ensemble Model for Drug Response Prediction in Precision Medicine Using Cancer Cell Lines Gene Expression.
Majumdar A; Liu Y; Lu Y; Wu S; Cheng L
Genes (Basel); 2021 May; 12(6):. PubMed ID: 34070793
[TBL] [Abstract][Full Text] [Related]
20. Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model.
Zhang N; Wang H; Fang Y; Wang J; Zheng X; Liu XS
PLoS Comput Biol; 2015; 11(9):e1004498. PubMed ID: 26418249
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
