These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

121 related articles for article (PubMed ID: 38856173)

  • 1. MANOCCA: a robust and computationally efficient test of covariance in high-dimension multivariate omics data.
    Boetto C; Frouin A; Henches L; Auvergne A; Suzuki Y; Patin E; Bredon M; Chiu A; Consortium MI; Sankararaman S; Zaitlen N; Kennedy SP; Quintana-Murci L; Duffy D; Sokol H; Aschard H
    Brief Bioinform; 2024 May; 25(4):. PubMed ID: 38856173
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Integrative analysis of gene expression and copy number alterations using canonical correlation analysis.
    Soneson C; Lilljebjörn H; Fioretos T; Fontes M
    BMC Bioinformatics; 2010 Apr; 11():191. PubMed ID: 20398334
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components.
    Park M; Kim D; Moon K; Park T
    Int J Mol Sci; 2020 Nov; 21(21):. PubMed ID: 33147797
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Improved covariance matrix estimators for weighted analysis of microarray data.
    Astrand M; Mostad P; Rudemo M
    J Comput Biol; 2007 Dec; 14(10):1353-67. PubMed ID: 18052774
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration.
    Pierre-Jean M; Deleuze JF; Le Floch E; Mauger F
    Brief Bioinform; 2020 Dec; 21(6):2011-2030. PubMed ID: 31792509
    [TBL] [Abstract][Full Text] [Related]  

  • 6. SMURC: High-Dimension Small-Sample Multivariate Regression With Covariance Estimation.
    Bayar B; Bouaynaya N; Shterenberg R
    IEEE J Biomed Health Inform; 2017 Mar; 21(2):573-581. PubMed ID: 26761909
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Classification for high-throughput data with an optimal subset of principal components.
    Song JJ; Ren Y; Yan F
    Comput Biol Chem; 2009 Oct; 33(5):408-13. PubMed ID: 19748831
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Accurate genetic and environmental covariance estimation with composite likelihood in genome-wide association studies.
    Gao B; Yang C; Liu J; Zhou X
    PLoS Genet; 2021 Jan; 17(1):e1009293. PubMed ID: 33395406
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Multivariate correlation estimator for inferring functional relationships from replicated genome-wide data.
    Zhu D; Li Y; Li H
    Bioinformatics; 2007 Sep; 23(17):2298-305. PubMed ID: 17586543
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes.
    Kim S; Herazo-Maya JD; Kang DD; Juan-Guardela BM; Tedrow J; Martinez FJ; Sciurba FC; Tseng GC; Kaminski N
    BMC Genomics; 2015 Nov; 16():924. PubMed ID: 26560100
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Part 1. Statistical Learning Methods for the Effects of Multiple Air Pollution Constituents.
    Coull BA; Bobb JF; Wellenius GA; Kioumourtzoglou MA; Mittleman MA; Koutrakis P; Godleski JJ
    Res Rep Health Eff Inst; 2015 Jun; (183 Pt 1-2):5-50. PubMed ID: 26333238
    [TBL] [Abstract][Full Text] [Related]  

  • 12. An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function.
    Yang JJ; Li J; Williams LK; Buu A
    BMC Bioinformatics; 2016 Jan; 17():19. PubMed ID: 26729364
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Computationally efficient, exact, covariate-adjusted genetic principal component analysis by leveraging individual marker summary statistics from large biobanks.
    Wolf JM; Barnard M; Xia X; Ryder N; Westra J; Tintle N
    Pac Symp Biocomput; 2020; 25():719-730. PubMed ID: 31797641
    [TBL] [Abstract][Full Text] [Related]  

  • 14. dCCA: detecting differential covariation patterns between two types of high-throughput omics data.
    Lee H; Ma T; Ke H; Ye Z; Chen S
    Brief Bioinform; 2024 May; 25(4):. PubMed ID: 38888456
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering.
    Chang J; Zhou W; Zhou WX; Wang L
    Biometrics; 2017 Mar; 73(1):31-41. PubMed ID: 27377648
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A global × global test for testing associations between two large sets of variables.
    Chaturvedi N; de Menezes RX; Goeman JJ
    Biom J; 2017 Jan; 59(1):145-158. PubMed ID: 27225065
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Multi-omics integration-a comparison of unsupervised clustering methodologies.
    Tini G; Marchetti L; Priami C; Scott-Boyer MP
    Brief Bioinform; 2019 Jul; 20(4):1269-1279. PubMed ID: 29272335
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data.
    Lin N; Zhu Y; Fan R; Xiong M
    PLoS Comput Biol; 2017 Oct; 13(10):e1005788. PubMed ID: 29040274
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Data reduction prior to inference: Are there consequences of comparing groups using a t-test based on principal component scores?
    Bedrick EJ
    Biometrics; 2020 Jun; 76(2):508-517. PubMed ID: 31584187
    [TBL] [Abstract][Full Text] [Related]  

  • 20.
    ; ; . PubMed ID:
    [No Abstract]   [Full Text] [Related]  

    [Next]    [New Search]
    of 7.