333 related articles for article (PubMed ID: 36434961)
1. Multi-omics data fusion using adaptive GTO guided Non-negative matrix factorization for cancer subtype discovery.
Bansal B; Sahoo A
Comput Methods Programs Biomed; 2023 Jan; 228():107246. PubMed ID: 36434961
[TBL] [Abstract][Full Text] [Related]
2. scMNMF: a novel method for single-cell multi-omics clustering based on matrix factorization.
Qiu Y; Guo D; Zhao P; Zou Q
Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38754408
[TBL] [Abstract][Full Text] [Related]
3. Randomized singular value decomposition for integrative subtype analysis of 'omics data' using non-negative matrix factorization.
Ni Y; He J; Chalise P
Stat Appl Genet Mol Biol; 2023 Jan; 22(1):. PubMed ID: 37937887
[TBL] [Abstract][Full Text] [Related]
4. Statistical Methods for Integrative Clustering of Multi-omics Data.
Chalise P; Kwon D; Fridley BL; Mo Q
Methods Mol Biol; 2023; 2629():73-93. PubMed ID: 36929074
[TBL] [Abstract][Full Text] [Related]
5. Capturing the latent space of an Autoencoder for multi-omics integration and cancer subtyping.
Madhumita ; Paul S
Comput Biol Med; 2022 Sep; 148():105832. PubMed ID: 35834966
[TBL] [Abstract][Full Text] [Related]
6. PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data.
Lemsara A; Ouadfel S; Fröhlich H
BMC Bioinformatics; 2020 Apr; 21(1):146. PubMed ID: 32299344
[TBL] [Abstract][Full Text] [Related]
7. Integrative clustering of multi-level omics data for disease subtype discovery using sequential double regularization.
Kim S; Oesterreich S; Kim S; Park Y; Tseng GC
Biostatistics; 2017 Jan; 18(1):165-179. PubMed ID: 27549122
[TBL] [Abstract][Full Text] [Related]
8. Autoencoder-assisted latent representation learning for survival prediction and multi-view clustering on multi-omics cancer subtyping.
Zhu S; Wang W; Fang W; Cui M
Math Biosci Eng; 2023 Nov; 20(12):21098-21119. PubMed ID: 38124589
[TBL] [Abstract][Full Text] [Related]
9. Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data.
Zhao J; Zhao B; Song X; Lyu C; Chen W; Xiong Y; Wei DQ
Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36702755
[TBL] [Abstract][Full Text] [Related]
10. Network-based integrative clustering of multiple types of genomic data using non-negative matrix factorization.
Chalise P; Ni Y; Fridley BL
Comput Biol Med; 2020 Mar; 118():103625. PubMed ID: 31999549
[TBL] [Abstract][Full Text] [Related]
11. PIntMF: Penalized Integrative Matrix Factorization method for multi-omics data.
Pierre-Jean M; Mauger F; Deleuze JF; Le Floch E
Bioinformatics; 2022 Jan; 38(4):900-907. PubMed ID: 34849583
[TBL] [Abstract][Full Text] [Related]
12. HCNM: Heterogeneous Correlation Network Model for Multi-level Integrative Study of Multi-omics Data for Cancer Subtype Prediction.
Vangimalla RR; Sreevalsan-Nair J
Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():1880-1886. PubMed ID: 34891654
[TBL] [Abstract][Full Text] [Related]
13. Cancer subtype identification by consensus guided graph autoencoders.
Liang C; Shang M; Luo J
Bioinformatics; 2021 Dec; 37(24):4779-4786. PubMed ID: 34289034
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Information-incorporated sparse convex clustering for disease subtyping.
Zhang X; Liu CT
Bioinformatics; 2023 Jul; 39(7):. PubMed ID: 37382570
[TBL] [Abstract][Full Text] [Related]
16. Evaluation of integrative clustering methods for the analysis of multi-omics data.
Chauvel C; Novoloaca A; Veyre P; Reynier F; Becker J
Brief Bioinform; 2020 Mar; 21(2):541-552. PubMed ID: 31220206
[TBL] [Abstract][Full Text] [Related]
17. Multi-omics clustering for cancer subtyping based on latent subspace learning.
Ye X; Shang Y; Shi T; Zhang W; Sakurai T
Comput Biol Med; 2023 Sep; 164():107223. PubMed ID: 37490833
[TBL] [Abstract][Full Text] [Related]
18. Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types.
Wu W; Zhang W; Ma X
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35043143
[TBL] [Abstract][Full Text] [Related]
19. Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping.
Ge S; Liu J; Cheng Y; Meng X; Wang X
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36445207
[TBL] [Abstract][Full Text] [Related]
20. Characterization of cancer subtypes associated with clinical outcomes by multi-omics integrative clustering.
Crippa V; Malighetti F; Villa M; Graudenzi A; Piazza R; Mologni L; Ramazzotti D
Comput Biol Med; 2023 Aug; 162():107064. PubMed ID: 37267828
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
