338 related articles for article (PubMed ID: 29403567)
1. Random Forest Missing Data Algorithms.
Tang F; Ishwaran H
Stat Anal Data Min; 2017 Dec; 10(6):363-377. PubMed ID: 29403567
[TBL] [Abstract][Full Text] [Related]
2. missForest with feature selection using binary particle swarm optimization improves the imputation accuracy of continuous data.
Jin H; Jung S; Won S
Genes Genomics; 2022 Jun; 44(6):651-658. PubMed ID: 35384632
[TBL] [Abstract][Full Text] [Related]
3. MissForest--non-parametric missing value imputation for mixed-type data.
Stekhoven DJ; Bühlmann P
Bioinformatics; 2012 Jan; 28(1):112-8. PubMed ID: 22039212
[TBL] [Abstract][Full Text] [Related]
4. Generative adversarial networks for imputing missing data for big data clinical research.
Dong W; Fong DYT; Yoon JS; Wan EYF; Bedford LE; Tang EHM; Lam CLK
BMC Med Res Methodol; 2021 Apr; 21(1):78. PubMed ID: 33879090
[TBL] [Abstract][Full Text] [Related]
5. The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model.
Guo CY; Yang YC; Chen YH
Front Public Health; 2021; 9():680054. PubMed ID: 34291028
[TBL] [Abstract][Full Text] [Related]
6. Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics.
Dekermanjian JP; Shaddox E; Nandy D; Ghosh D; Kechris K
BMC Bioinformatics; 2022 May; 23(1):179. PubMed ID: 35578165
[TBL] [Abstract][Full Text] [Related]
7. Imputing missing covariates in time-to-event analysis within distributed research networks: A simulation study.
Li D; Wong J; Li X; Toh S; Wang R
Pharmacoepidemiol Drug Saf; 2023 Mar; 32(3):330-340. PubMed ID: 36380400
[TBL] [Abstract][Full Text] [Related]
8. Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study.
Kokla M; Virtanen J; Kolehmainen M; Paananen J; Hanhineva K
BMC Bioinformatics; 2019 Oct; 20(1):492. PubMed ID: 31601178
[TBL] [Abstract][Full Text] [Related]
9. Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction.
Hong S; Lynn HS
BMC Med Res Methodol; 2020 Jul; 20(1):199. PubMed ID: 32711455
[TBL] [Abstract][Full Text] [Related]
10. Performance of Multiple Imputation Using Modern Machine Learning Methods in Electronic Health Records Data.
Getz K; Hubbard RA; Linn KA
Epidemiology; 2023 Mar; 34(2):206-215. PubMed ID: 36722803
[TBL] [Abstract][Full Text] [Related]
11. Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study.
Shah AD; Bartlett JW; Carpenter J; Nicholas O; Hemingway H
Am J Epidemiol; 2014 Mar; 179(6):764-74. PubMed ID: 24589914
[TBL] [Abstract][Full Text] [Related]
12. Analyzing the Effect of Imputation on Classification Performance under MCAR and MAR Missing Mechanisms.
Buczak P; Chen JJ; Pauly M
Entropy (Basel); 2023 Mar; 25(3):. PubMed ID: 36981409
[TBL] [Abstract][Full Text] [Related]
13. Extremely missing numerical data in Electronic Health Records for machine learning can be managed through simple imputation methods considering informative missingness: A comparative of solutions in a COVID-19 mortality case study.
Ferri P; Romero-Garcia N; Badenes R; Lora-Pablos D; Morales TG; Gómez de la Cámara A; García-Gómez JM; Sáez C
Comput Methods Programs Biomed; 2023 Dec; 242():107803. PubMed ID: 37703700
[TBL] [Abstract][Full Text] [Related]
14. Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets.
Li J; Guo S; Ma R; He J; Zhang X; Rui D; Ding Y; Li Y; Jian L; Cheng J; Guo H
BMC Med Res Methodol; 2024 Feb; 24(1):41. PubMed ID: 38365610
[TBL] [Abstract][Full Text] [Related]
15. Comparison of imputation methods for missing production data of dairy cattle.
You J; Ellis JL; Adams S; Sahar M; Jacobs M; Tulpan D
Animal; 2023 Dec; 17 Suppl 5():100921. PubMed ID: 37659911
[TBL] [Abstract][Full Text] [Related]
16. Missing value imputation in high-dimensional phenomic data: imputable or not, and how?
Liao SG; Lin Y; Kang DD; Chandra D; Bon J; Kaminski N; Sciurba FC; Tseng GC
BMC Bioinformatics; 2014 Nov; 15(1):346. PubMed ID: 25371041
[TBL] [Abstract][Full Text] [Related]
17. Missing Value Estimation using Clustering and Deep Learning within Multiple Imputation Framework.
Samad MD; Abrar S; Diawara N
Knowl Based Syst; 2022 Aug; 249():. PubMed ID: 36159738
[TBL] [Abstract][Full Text] [Related]
18. Comparison of Missing Data Infilling Mechanisms for Recovering a Real-World Single Station Streamflow Observation.
Baddoo TD; Li Z; Odai SN; Boni KRC; Nooni IK; Andam-Akorful SA
Int J Environ Res Public Health; 2021 Aug; 18(16):. PubMed ID: 34444127
[TBL] [Abstract][Full Text] [Related]
19. Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data.
Mir AA; Kearfott KJ; Çelebi FV; Rafique M
PLoS One; 2022; 17(1):e0262131. PubMed ID: 35025953
[TBL] [Abstract][Full Text] [Related]
20. Missing value imputation in proximity extension assay-based targeted proteomics data.
Lenz M; Schulz A; Koeck T; Rapp S; Nagler M; Sauer M; Eggebrecht L; Ten Cate V; Panova-Noeva M; Prochaska JH; Lackner KJ; Münzel T; Leineweber K; Wild PS; Andrade-Navarro MA
PLoS One; 2020; 15(12):e0243487. PubMed ID: 33315883
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]