174 related articles for article (PubMed ID: 37406589)
1. 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]
2. 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]
3. AVBAE-MODFR: A novel deep learning framework of embedding and feature selection on multi-omics data for pan-cancer classification.
Li M; Guo H; Wang K; Kang C; Yin Y; Zhang H
Comput Biol Med; 2024 Jul; 177():108614. PubMed ID: 38796884
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Local augmented graph neural network for multi-omics cancer prognosis prediction and analysis.
Zhang Y; Xiong S; Wang Z; Liu Y; Luo H; Li B; Zou Q
Methods; 2023 May; 213():1-9. PubMed ID: 36933628
[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. A multi-omics supervised autoencoder for pan-cancer clinical outcome endpoints prediction.
Tan K; Huang W; Hu J; Dong S
BMC Med Inform Decis Mak; 2020 Jul; 20(Suppl 3):129. PubMed ID: 32646413
[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. MMCL-CDR: enhancing cancer drug response prediction with multi-omics and morphology images contrastive representation learning.
Li Y; Guo Z; Gao X; Wang G
Bioinformatics; 2023 Dec; 39(12):. PubMed ID: 38070154
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Hi-GeoMVP: a hierarchical geometry-enhanced deep learning model for drug response prediction.
Chen Y; Zhang L
Bioinformatics; 2024 Mar; 40(4):. PubMed ID: 38614131
[TBL] [Abstract][Full Text] [Related]
12. MCluster-VAEs: An end-to-end variational deep learning-based clustering method for subtype discovery using multi-omics data.
Rong Z; Liu Z; Song J; Cao L; Yu Y; Qiu M; Hou Y
Comput Biol Med; 2022 Nov; 150():106085. PubMed ID: 36162197
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. 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]
16. 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 May; PP():. PubMed ID: 38776197
[TBL] [Abstract][Full Text] [Related]
17. [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]
18. 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]
19. 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]
20. Deeply integrating latent consistent representations in high-noise multi-omics data for cancer subtyping.
Cai Y; Wang S
Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38426322
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
