537 related articles for article (PubMed ID: 32763377)
1. 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]
2. 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]
3. 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]
4. A prognostic prediction model for ovarian cancer using a cross-modal view correlation discovery network.
Wang H; Han X; Ren J; Cheng H; Li H; Li Y; Li X
Math Biosci Eng; 2024 Jan; 21(1):736-764. PubMed ID: 38303441
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. 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]
8. 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]
9. 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]
10. Pathway Relevance Ranking for Tumor Samples through Network-Based Data Integration.
Verbeke LP; Van den Eynden J; Fierro AC; Demeester P; Fostier J; Marchal K
PLoS One; 2015; 10(7):e0133503. PubMed ID: 26217958
[TBL] [Abstract][Full Text] [Related]
11. Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.
Liu C; Wang X; Genchev GZ; Lu H
Methods; 2017 Jul; 124():100-107. PubMed ID: 28627406
[TBL] [Abstract][Full Text] [Related]
12. Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies.
Kim SY; Jeong HH; Kim J; Moon JH; Sohn KA
Biol Direct; 2019 Apr; 14(1):8. PubMed ID: 31036036
[TBL] [Abstract][Full Text] [Related]
13. Integrated multi-omics analysis of ovarian cancer using variational autoencoders.
Hira MT; Razzaque MA; Angione C; Scrivens J; Sawan S; Sarker M
Sci Rep; 2021 Mar; 11(1):6265. PubMed ID: 33737557
[TBL] [Abstract][Full Text] [Related]
14. A Translational Pipeline for Overall Survival Prediction of Breast Cancer Patients by Decision-Level Integration of Multi-Omics Data.
Mitchel J; Chatlin K; Tong L; Wang MD
Proceedings (IEEE Int Conf Bioinformatics Biomed); 2019 Nov; 2019():1573-1580. PubMed ID: 32601549
[TBL] [Abstract][Full Text] [Related]
15. Integrated multi-omics analysis of genomics, epigenomics, and transcriptomics in ovarian carcinoma.
Zheng M; Hu Y; Gou R; Wang J; Nie X; Li X; Liu Q; Liu J; Lin B
Aging (Albany NY); 2019 Jun; 11(12):4198-4215. PubMed ID: 31257224
[TBL] [Abstract][Full Text] [Related]
16. Incorporating inter-relationships between different levels of genomic data into cancer clinical outcome prediction.
Kim D; Shin H; Sohn KA; Verma A; Ritchie MD; Kim JH
Methods; 2014 Jun; 67(3):344-53. PubMed ID: 24561168
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction.
Kim D; Joung JG; Sohn KA; Shin H; Park YR; Ritchie MD; Kim JH
J Am Med Inform Assoc; 2015 Jan; 22(1):109-20. PubMed ID: 25002459
[TBL] [Abstract][Full Text] [Related]
19. MMDAE-HGSOC: A novel method for high-grade serous ovarian cancer molecular subtypes classification based on multi-modal deep autoencoder.
Wang HQ; Li HL; Han JL; Feng ZP; Deng HX; Han X
Comput Biol Chem; 2023 Aug; 105():107906. PubMed ID: 37336028
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
