315 related articles for article (PubMed ID: 29385130)
1. GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies.
Wei R; Wang J; Jia E; Chen T; Ni Y; Jia W
PLoS Comput Biol; 2018 Jan; 14(1):e1005973. PubMed ID: 29385130
[TBL] [Abstract][Full Text] [Related]
2. Left-Censored Missing Value Imputation Approach for MS-Based Proteomics Data with GSimp.
Wei R; Wang J
Methods Mol Biol; 2023; 2426():119-129. PubMed ID: 36308687
[TBL] [Abstract][Full Text] [Related]
3. Improved GSimp: A Flexible Missing Value Imputation Method to Support Regulatory Bioequivalence Assessment.
Wang J; Gong X; Hu M; Zhao L
Ann Biomed Eng; 2023 Jan; 51(1):163-173. PubMed ID: 36107365
[TBL] [Abstract][Full Text] [Related]
4. Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data.
Wei R; Wang J; Su M; Jia E; Chen S; Chen T; Ni Y
Sci Rep; 2018 Jan; 8(1):663. PubMed ID: 29330539
[TBL] [Abstract][Full Text] [Related]
5. NS-kNN: a modified k-nearest neighbors approach for imputing metabolomics data.
Lee JY; Styczynski MP
Metabolomics; 2018 Nov; 14(12):153. PubMed ID: 30830437
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. GMSimpute: a generalized two-step Lasso approach to impute missing values in label-free mass spectrum analysis.
Li Q; Fisher K; Meng W; Fang B; Welsh E; Haura EB; Koomen JM; Eschrich SA; Fridley BL; Chen YA
Bioinformatics; 2020 Jan; 36(1):257-263. PubMed ID: 31199438
[TBL] [Abstract][Full Text] [Related]
8. Addressing Missing Data in GC × GC Metabolomics: Identifying Missingness Type and Evaluating the Impact of Imputation Methods on Experimental Replication.
Davis TJ; Firzli TR; Higgins Keppler EA; Richardson M; Bean HD
Anal Chem; 2022 Aug; 94(31):10912-10920. PubMed ID: 35881554
[TBL] [Abstract][Full Text] [Related]
9. Kernel weighted least square approach for imputing missing values of metabolomics data.
Kumar N; Hoque MA; Sugimoto M
Sci Rep; 2021 May; 11(1):11108. PubMed ID: 34045614
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. rMisbeta: A robust missing value imputation approach in transcriptomics and metabolomics data.
Shahjaman M; Rahman MR; Islam T; Auwul MR; Moni MA; Mollah MNH
Comput Biol Med; 2021 Nov; 138():104911. PubMed ID: 34634637
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. NMF-Based Approach for Missing Values Imputation of Mass Spectrometry Metabolomics Data.
Xu J; Wang Y; Xu X; Cheng KK; Raftery D; Dong J
Molecules; 2021 Sep; 26(19):. PubMed ID: 34641330
[TBL] [Abstract][Full Text] [Related]
14. BayesMetab: treatment of missing values in metabolomic studies using a Bayesian modeling approach.
Shah J; Brock GN; Gaskins J
BMC Bioinformatics; 2019 Dec; 20(Suppl 24):673. PubMed ID: 31861984
[TBL] [Abstract][Full Text] [Related]
15. Distribution based nearest neighbor imputation for truncated high dimensional data with applications to pre-clinical and clinical metabolomics studies.
Shah JS; Rai SN; DeFilippis AP; Hill BG; Bhatnagar A; Brock GN
BMC Bioinformatics; 2017 Feb; 18(1):114. PubMed ID: 28219348
[TBL] [Abstract][Full Text] [Related]
16. Clustering with missing and left-censored data: A simulation study comparing multiple-imputation-based procedures.
Faucheux L; Resche-Rigon M; Curis E; Soumelis V; Chevret S
Biom J; 2021 Feb; 63(2):372-393. PubMed ID: 32627864
[TBL] [Abstract][Full Text] [Related]
17. Missing value imputation for microarray data: a comprehensive comparison study and a web tool.
Chiu CC; Chan SY; Wang CC; Wu WS
BMC Syst Biol; 2013; 7 Suppl 6(Suppl 6):S12. PubMed ID: 24565220
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. ProJect: a powerful mixed-model missing value imputation method.
Kong W; Wong BJH; Hui HWH; Lim KP; Wang Y; Wong L; Goh WWB
Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37419612
[TBL] [Abstract][Full Text] [Related]
20. Heckman imputation models for binary or continuous MNAR outcomes and MAR predictors.
Galimard JE; Chevret S; Curis E; Resche-Rigon M
BMC Med Res Methodol; 2018 Aug; 18(1):90. PubMed ID: 30170561
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]