441 related articles for article (PubMed ID: 32234433)
1. Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration.
Seal DB; Das V; Goswami S; De RK
Genomics; 2020 Jul; 112(4):2833-2841. PubMed ID: 32234433
[TBL] [Abstract][Full Text] [Related]
2. DeFusion: a denoised network regularization framework for multi-omics integration.
Wang W; Zhang X; Dai DQ
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33822879
[TBL] [Abstract][Full Text] [Related]
3. Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer.
Tong L; Wu H; Wang MD
Methods; 2021 May; 189():74-85. PubMed ID: 32763377
[TBL] [Abstract][Full Text] [Related]
4. Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis.
Tong L; Mitchel J; Chatlin K; Wang MD
BMC Med Inform Decis Mak; 2020 Sep; 20(1):225. PubMed ID: 32933515
[TBL] [Abstract][Full Text] [Related]
5. Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profiles.
Kaur H; Bhalla S; Raghava GPS
PLoS One; 2019; 14(9):e0221476. PubMed ID: 31490960
[TBL] [Abstract][Full Text] [Related]
6. A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification.
Chung RH; Kang CY
Gigascience; 2019 May; 8(5):. PubMed ID: 31029063
[TBL] [Abstract][Full Text] [Related]
7. Multi-omics Analysis of Primary Cell Culture Models Reveals Genetic and Epigenetic Basis of Intratumoral Phenotypic Diversity.
Liu S; Yang Z; Li G; Li C; Luo Y; Gong Q; Wu X; Li T; Zhang Z; Xing B; Xu X; Lu X
Genomics Proteomics Bioinformatics; 2019 Dec; 17(6):576-589. PubMed ID: 32205176
[TBL] [Abstract][Full Text] [Related]
8. Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer.
Chaudhary K; Poirion OB; Lu L; Garmire LX
Clin Cancer Res; 2018 Mar; 24(6):1248-1259. PubMed ID: 28982688
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas.
Hou Y; Guo H; Cao C; Li X; Hu B; Zhu P; Wu X; Wen L; Tang F; Huang Y; Peng J
Cell Res; 2016 Mar; 26(3):304-19. PubMed ID: 26902283
[TBL] [Abstract][Full Text] [Related]
11. Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features.
Xie G; Dong C; Kong Y; Zhong JF; Li M; Wang K
Genes (Basel); 2019 Mar; 10(3):. PubMed ID: 30901858
[TBL] [Abstract][Full Text] [Related]
12. Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data.
Lin Y; Zhang W; Cao H; Li G; Du W
Genes (Basel); 2020 Aug; 11(8):. PubMed ID: 32759821
[TBL] [Abstract][Full Text] [Related]
13. Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer.
Kim D; Li R; Dudek SM; Ritchie MD
J Biomed Inform; 2015 Aug; 56():220-8. PubMed ID: 26048077
[TBL] [Abstract][Full Text] [Related]
14. Integrating multi-omics data through deep learning for accurate cancer prognosis prediction.
Chai H; Zhou X; Zhang Z; Rao J; Zhao H; Yang Y
Comput Biol Med; 2021 Jul; 134():104481. PubMed ID: 33989895
[TBL] [Abstract][Full Text] [Related]
15. A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping.
Sathyanarayanan A; Gupta R; Thompson EW; Nyholt DR; Bauer DC; Nagaraj SH
Brief Bioinform; 2020 Dec; 21(6):1920-1936. PubMed ID: 31774481
[TBL] [Abstract][Full Text] [Related]
16. Integrative analysis of genomic and epigenomic regulation of the transcriptome in liver cancer.
Woo HG; Choi JH; Yoon S; Jee BA; Cho EJ; Lee JH; Yu SJ; Yoon JH; Yi NJ; Lee KW; Suh KS; Kim YJ
Nat Commun; 2017 Oct; 8(1):839. PubMed ID: 29018224
[TBL] [Abstract][Full Text] [Related]
17. A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data.
Xu J; Wu P; Chen Y; Meng Q; Dawood H; Dawood H
BMC Bioinformatics; 2019 Oct; 20(1):527. PubMed ID: 31660856
[TBL] [Abstract][Full Text] [Related]
18. Integrate multi-omics data with biological interaction networks using Multi-view Factorization AutoEncoder (MAE).
Ma T; Zhang A
BMC Genomics; 2019 Dec; 20(Suppl 11):944. PubMed ID: 31856727
[TBL] [Abstract][Full Text] [Related]
19. Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data.
Takahashi S; Asada K; Takasawa K; Shimoyama R; Sakai A; Bolatkan A; Shinkai N; Kobayashi K; Komatsu M; Kaneko S; Sese J; Hamamoto R
Biomolecules; 2020 Oct; 10(10):. PubMed ID: 33086649
[TBL] [Abstract][Full Text] [Related]
20. MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model.
Zhong Y; Peng Y; Lin Y; Chen D; Zhang H; Zheng W; Chen Y; Wu C
BMC Med Inform Decis Mak; 2023 May; 23(1):82. PubMed ID: 37147619
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
