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 *

278 related articles for article (PubMed ID: 31248362)

  • 1. Block Forests: random forests for blocks of clinical and omics covariate data.
    Hornung R; Wright MN
    BMC Bioinformatics; 2019 Jun; 20(1):358. PubMed ID: 31248362
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Large-scale benchmark study of survival prediction methods using multi-omics data.
    Herrmann M; Probst P; Hornung R; Jurinovic V; Boulesteix AL
    Brief Bioinform; 2021 May; 22(3):. PubMed ID: 32823283
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data.
    Nasejje JB; Mwambi H; Dheda K; Lesosky M
    BMC Med Res Methodol; 2017 Jul; 17(1):115. PubMed ID: 28754093
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A comparative study of forest methods for time-to-event data: variable selection and predictive performance.
    Liu Y; Zhou S; Wei H; An S
    BMC Med Res Methodol; 2021 Sep; 21(1):193. PubMed ID: 34563138
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Unbiased split variable selection for random survival forests using maximally selected rank statistics.
    Wright MN; Dankowski T; Ziegler A
    Stat Med; 2017 Apr; 36(8):1272-1284. PubMed ID: 28088842
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Prediction of
    Ingrisch M; Schöppe F; Paprottka K; Fabritius M; Strobl FF; De Toni EN; Ilhan H; Todica A; Michl M; Paprottka PM
    J Nucl Med; 2018 May; 59(5):769-773. PubMed ID: 29146692
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Comparisons of Forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Machine Learning Models based on Multi-omics.
    Mo L; Su Y; Yuan J; Xiao Z; Zhang Z; Lan X; Huang D
    Curr Genomics; 2022 Jun; 23(2):94-108. PubMed ID: 36778975
    [No Abstract]   [Full Text] [Related]  

  • 8. Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests.
    Nguyen TT; Huang J; Wu Q; Nguyen T; Li M
    BMC Genomics; 2015; 16 Suppl 2(Suppl 2):S5. PubMed ID: 25708662
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Benchmark study of feature selection strategies for multi-omics data.
    Li Y; Mansmann U; Du S; Hornung R
    BMC Bioinformatics; 2022 Oct; 23(1):412. PubMed ID: 36199022
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study.
    Li Y; Herold T; Mansmann U; Hornung R
    BMC Med Inform Decis Mak; 2024 Sep; 24(1):244. PubMed ID: 39223659
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework.
    Bussy S; Veil R; Looten V; Burgun A; Gaïffas S; Guilloux A; Ranque B; Jannot AS
    BMC Med Res Methodol; 2019 Mar; 19(1):50. PubMed ID: 30841867
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Application of random survival forests in understanding the determinants of under-five child mortality in Uganda in the presence of covariates that satisfy the proportional and non-proportional hazards assumption.
    Nasejje JB; Mwambi H
    BMC Res Notes; 2017 Sep; 10(1):459. PubMed ID: 28882171
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Integration of multi-omics data for prediction of phenotypic traits using random forest.
    Acharjee A; Kloosterman B; Visser RG; Maliepaard C
    BMC Bioinformatics; 2016 Jun; 17 Suppl 5(Suppl 5):180. PubMed ID: 27295212
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Evaluation of variable selection methods for random forests and omics data sets.
    Degenhardt F; Seifert S; Szymczak S
    Brief Bioinform; 2019 Mar; 20(2):492-503. PubMed ID: 29045534
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Clinical risk prediction with random forests for survival, longitudinal, and multivariate (RF-SLAM) data analysis.
    Wongvibulsin S; Wu KC; Zeger SL
    BMC Med Res Methodol; 2019 Dec; 20(1):1. PubMed ID: 31888507
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A random forest approach for competing risks based on pseudo-values.
    Mogensen UB; Gerds TA
    Stat Med; 2013 Aug; 32(18):3102-14. PubMed ID: 23508720
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Deep learning assisted multi-omics integration for survival and drug-response prediction in breast cancer.
    Malik V; Kalakoti Y; Sundar D
    BMC Genomics; 2021 Mar; 22(1):214. PubMed ID: 33761889
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Survival prediction models: an introduction to discrete-time modeling.
    Suresh K; Severn C; Ghosh D
    BMC Med Res Methodol; 2022 Jul; 22(1):207. PubMed ID: 35883032
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data.
    Takahashi S; Asada K; Takasawa K; Shimoyama R; Sakai A; Bolatkan A; Shinkai N; Kobayashi K; Komatsu M; Kaneko S; Sese J; Hamamoto R
    Biomolecules; 2020 Oct; 10(10):. PubMed ID: 33086649
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 14.