231 related articles for article (PubMed ID: 34507532)
1. SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures.
Zuo Z; Wang P; Chen X; Tian L; Ge H; Qian D
BMC Bioinformatics; 2021 Sep; 22(1):434. PubMed ID: 34507532
[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. 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]
4. 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]
5. De novo Prediction of Cell-Drug Sensitivities Using Deep Learning-based Graph Regularized Matrix Factorization.
Ren S; Tao Y; Yu K; Xue Y; Schwartz R; Lu X
Pac Symp Biocomput; 2022; 27():278-289. PubMed ID: 34890156
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning.
Pang W; Chen M; Qin Y
BMC Bioinformatics; 2024 May; 25(1):182. PubMed ID: 38724920
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
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. Improving anti-cancer drug response prediction using multi-task learning on graph convolutional networks.
Liu H; Peng W; Dai W; Lin J; Fu X; Liu L; Liu L; Yu N
Methods; 2024 Feb; 222():41-50. PubMed ID: 38157919
[TBL] [Abstract][Full Text] [Related]
12. XGraphCDS: An explainable deep learning model for predicting drug sensitivity from gene pathways and chemical structures.
Wang Y; Yu X; Gu Y; Li W; Zhu K; Chen L; Tang Y; Liu G
Comput Biol Med; 2024 Jan; 168():107746. PubMed ID: 38039896
[TBL] [Abstract][Full Text] [Related]
13. De Novo Molecule Design by Translating from Reduced Graphs to SMILES.
Pogány P; Arad N; Genway S; Pickett SD
J Chem Inf Model; 2019 Mar; 59(3):1136-1146. PubMed ID: 30525594
[TBL] [Abstract][Full Text] [Related]
14. Network-based drug sensitivity prediction.
Ahmed KT; Park S; Jiang Q; Yeu Y; Hwang T; Zhang W
BMC Med Genomics; 2020 Dec; 13(Suppl 11):193. PubMed ID: 33371891
[TBL] [Abstract][Full Text] [Related]
15. Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response.
Su R; Liu X; Wei L; Zou Q
Methods; 2019 Aug; 166():91-102. PubMed ID: 30772464
[TBL] [Abstract][Full Text] [Related]
16. Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders.
Manica M; Oskooei A; Born J; Subramanian V; Sáez-Rodríguez J; Rodríguez Martínez M
Mol Pharm; 2019 Dec; 16(12):4797-4806. PubMed ID: 31618586
[TBL] [Abstract][Full Text] [Related]
17. Graph Convolutional Networks for Drug Response Prediction.
Nguyen T; Nguyen GTT; Nguyen T; Le DH
IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(1):146-154. PubMed ID: 33606633
[TBL] [Abstract][Full Text] [Related]
18. DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration.
Wang Y; Yang Y; Chen S; Wang J
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33822890
[TBL] [Abstract][Full Text] [Related]
19. Deep learning of mutation-gene-drug relations from the literature.
Lee K; Kim B; Choi Y; Kim S; Shin W; Lee S; Park S; Kim S; Tan AC; Kang J
BMC Bioinformatics; 2018 Jan; 19(1):21. PubMed ID: 29368597
[TBL] [Abstract][Full Text] [Related]
20. Deep Convolutional Neural Networks for the Prediction of Molecular Properties: Challenges and Opportunities Connected to the Data.
Ståhl N; Falkman G; Karlsson A; Mathiason G; Boström J
J Integr Bioinform; 2018 Dec; 16(1):. PubMed ID: 30517077
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
