180 related articles for article (PubMed ID: 38303441)
1. 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]
2. 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]
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. 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]
5. 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]
6. 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]
7. 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]
8. 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]
9. 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]
10. 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]
11. 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]
12. Assessing the clinical utility of multi-omics data for predicting serous ovarian cancer prognosis.
Zhang Z; Wei Z; Zhao L; Gu C; Meng Y
J Obstet Gynaecol; 2023 Dec; 43(1):2171778. PubMed ID: 36803381
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration.
Yin C; Cao Y; Sun P; Zhang H; Li Z; Xu Y; Sun H
Front Genet; 2022; 13():884028. PubMed ID: 35646077
[TBL] [Abstract][Full Text] [Related]
15. A novel prognostic model based on multi-omics features predicts the prognosis of colon cancer patients.
Yang H; Jin W; Liu H; Wang X; Wu J; Gan D; Cui C; Han Y; Han C; Wang Z
Mol Genet Genomic Med; 2020 Jul; 8(7):e1255. PubMed ID: 32396280
[TBL] [Abstract][Full Text] [Related]
16. Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer.
Zeng H; Chen L; Zhang M; Luo Y; Ma X
Gynecol Oncol; 2021 Oct; 163(1):171-180. PubMed ID: 34275655
[TBL] [Abstract][Full Text] [Related]
17. Deep multi-modal fusion network with gated unit for breast cancer survival prediction.
Yuan H; Xu H
Comput Methods Biomech Biomed Engin; 2024 May; 27(7):883-896. PubMed ID: 37166185
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Using knowledge-driven genomic interactions for multi-omics data analysis: metadimensional models for predicting clinical outcomes in ovarian carcinoma.
Kim D; Li R; Lucas A; Verma SS; Dudek SM; Ritchie MD
J Am Med Inform Assoc; 2017 May; 24(3):577-587. PubMed ID: 28040685
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]