138 related articles for article (PubMed ID: 36816042)
1. 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]
2. 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]
3. Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model.
Emdadi A; Eslahchi C
BMC Bioinformatics; 2021 Jan; 22(1):33. PubMed ID: 33509079
[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. 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]
6. A quantile regression forest based method to predict drug response and assess prediction reliability.
Fang Y; Xu P; Yang J; Qin Y
PLoS One; 2018; 13(10):e0205155. PubMed ID: 30289891
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. 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]
10. 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]
11. Text-mining-based feature selection for anticancer drug response prediction.
Wu G; Zaker A; Ebrahimi A; Tripathi S; Mer AS
Bioinform Adv; 2024; 4(1):vbae047. PubMed ID: 38606185
[TBL] [Abstract][Full Text] [Related]
12. Predicting Anticancer Drug Response With Deep Learning Constrained by Signaling Pathways.
Zhang H; Chen Y; Li F
Front Bioinform; 2021; 1():639349. PubMed ID: 36303766
[TBL] [Abstract][Full Text] [Related]
13. Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection.
Dong Z; Zhang N; Li C; Wang H; Fang Y; Wang J; Zheng X
BMC Cancer; 2015 Jun; 15():489. PubMed ID: 26121976
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Deep learning assisted multi-omics integration for survival and drug-response prediction in breast cancer.
Malik V; Kalakoti Y; Sundar D
BMC Genomics; 2021 Mar; 22(1):214. PubMed ID: 33761889
[TBL] [Abstract][Full Text] [Related]
16. A universal deep learning approach for modeling the flow of patients under different severities.
Jiang S; Chin KS; Tsui KL
Comput Methods Programs Biomed; 2018 Feb; 154():191-203. PubMed ID: 29249343
[TBL] [Abstract][Full Text] [Related]
17. Seminal quality prediction using data mining methods.
Sahoo AJ; Kumar Y
Technol Health Care; 2014; 22(4):531-45. PubMed ID: 24898862
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis.
Brindha GR; Rishiikeshwer BS; Santhi B; Nakendraprasath K; Manikandan R; Gandomi AH
Comput Methods Programs Biomed; 2022 Sep; 224():107027. PubMed ID: 35914385
[TBL] [Abstract][Full Text] [Related]
20. Development and comparative analysis of ANN and SVR-based models with conventional regression models for predicting spray drift.
Moges G; McDonnell K; Delele MA; Ali AN; Fanta SW
Environ Sci Pollut Res Int; 2023 Feb; 30(8):21927-21944. PubMed ID: 36280637
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]