119 related articles for article (PubMed ID: 35441661)
1. iSFun: an R package for integrative dimension reduction analysis.
Fang K; Ren R; Zhang Q; Ma S
Bioinformatics; 2022 May; 38(11):3134-3135. PubMed ID: 35441661
[TBL] [Abstract][Full Text] [Related]
2. Integrative sparse principal component analysis of gene expression data.
Liu M; Fan X; Fang K; Zhang Q; Ma S
Genet Epidemiol; 2017 Dec; 41(8):844-865. PubMed ID: 29114920
[TBL] [Abstract][Full Text] [Related]
3. integrOmics: an R package to unravel relationships between two omics datasets.
Lê Cao KA; González I; Déjean S
Bioinformatics; 2009 Nov; 25(21):2855-6. PubMed ID: 19706745
[TBL] [Abstract][Full Text] [Related]
4. Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data.
Bastien P; Bertrand F; Meyer N; Maumy-Bertrand M
Bioinformatics; 2015 Feb; 31(3):397-404. PubMed ID: 25286920
[TBL] [Abstract][Full Text] [Related]
5. Meta-analytic principal component analysis in integrative omics application.
Kim S; Kang D; Huo Z; Park Y; Tseng GC
Bioinformatics; 2018 Apr; 34(8):1321-1328. PubMed ID: 29186328
[TBL] [Abstract][Full Text] [Related]
6. Multivariate Analysis with the R Package mixOmics.
Welham Z; Déjean S; Lê Cao KA
Methods Mol Biol; 2023; 2426():333-359. PubMed ID: 36308696
[TBL] [Abstract][Full Text] [Related]
7. Integrative sparse partial least squares.
Liang W; Ma S; Zhang Q; Zhu T
Stat Med; 2021 Apr; 40(9):2239-2256. PubMed ID: 33559203
[TBL] [Abstract][Full Text] [Related]
8. HeteroGGM: an R package for Gaussian graphical model-based heterogeneity analysis.
Ren M; Zhang S; Zhang Q; Ma S
Bioinformatics; 2021 Sep; 37(18):3073-3074. PubMed ID: 33638346
[TBL] [Abstract][Full Text] [Related]
9. PCAmatchR: a flexible R package for optimal case-control matching using weighted principal components.
Brown DW; Myers TA; Machiela MJ
Bioinformatics; 2021 May; 37(8):1178-1181. PubMed ID: 32926120
[TBL] [Abstract][Full Text] [Related]
10. NCutYX: a package for clustering analysis of multilayer omics data.
Teran Hidalgo SJ; Wu M; Ma S
Bioinformatics; 2019 Nov; 36(6):1976-7. PubMed ID: 31730176
[TBL] [Abstract][Full Text] [Related]
11. iDINGO-integrative differential network analysis in genomics with Shiny application.
Class CA; Ha MJ; Baladandayuthapani V; Do KA
Bioinformatics; 2018 Apr; 34(7):1243-1245. PubMed ID: 29194470
[TBL] [Abstract][Full Text] [Related]
12. AIME: Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments.
Yu T
PLoS Comput Biol; 2022 Jan; 18(1):e1009826. PubMed ID: 35081109
[TBL] [Abstract][Full Text] [Related]
13. Integrating omics datasets with the OmicsPLS package.
Bouhaddani SE; Uh HW; Jongbloed G; Hayward C; Klarić L; Kiełbasa SM; Houwing-Duistermaat J
BMC Bioinformatics; 2018 Oct; 19(1):371. PubMed ID: 30309317
[TBL] [Abstract][Full Text] [Related]
14. Penalized co-inertia analysis with applications to -omics data.
Min EJ; Safo SE; Long Q
Bioinformatics; 2019 Mar; 35(6):1018-1025. PubMed ID: 30165424
[TBL] [Abstract][Full Text] [Related]
15. biospear: an R package for biomarker selection in penalized Cox regression.
Ternès N; Rotolo F; Michiels S
Bioinformatics; 2018 Jan; 34(1):112-113. PubMed ID: 28927242
[TBL] [Abstract][Full Text] [Related]
16. Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data.
Ferragina A; de los Campos G; Vazquez AI; Cecchinato A; Bittante G
J Dairy Sci; 2015 Nov; 98(11):8133-51. PubMed ID: 26387015
[TBL] [Abstract][Full Text] [Related]
17. Conditional canonical correlation estimation based on covariates with random forests.
Alakuş C; Larocque D; Jacquemont S; Barlaam F; Martin CO; Agbogba K; Lippé S; Labbe A
Bioinformatics; 2021 Sep; 37(17):2714-2721. PubMed ID: 33693547
[TBL] [Abstract][Full Text] [Related]
18. MOSS: multi-omic integration with sparse value decomposition.
Gonzalez-Reymundez A; Grueneberg A; Lu G; Alves FC; Rincon G; Vazquez AI
Bioinformatics; 2022 May; 38(10):2956-2958. PubMed ID: 35561193
[TBL] [Abstract][Full Text] [Related]
19. Benefits of dimension reduction in penalized regression methods for high-dimensional grouped data: a case study in low sample size.
Ajana S; Acar N; Bretillon L; Hejblum BP; Jacqmin-Gadda H; Delcourt C;
Bioinformatics; 2019 Oct; 35(19):3628-3634. PubMed ID: 30931473
[TBL] [Abstract][Full Text] [Related]
20. GEInfo: an R package for gene-environment interaction analysis incorporating prior information.
Wang X; Liu H; Ma S
Bioinformatics; 2022 May; 38(11):3139-3140. PubMed ID: 35485739
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]