Format
Sort by
Items per page

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 111

1.

Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies.

Lazar C, Gatto L, Ferro M, Bruley C, Burger T.

J Proteome Res. 2016 Apr 1;15(4):1116-25. doi: 10.1021/acs.jproteome.5b00981. Epub 2016 Mar 1.

2.

Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics.

Webb-Robertson BJ, Wiberg HK, Matzke MM, Brown JN, Wang J, McDermott JE, Smith RD, Rodland KD, Metz TO, Pounds JG, Waters KM.

J Proteome Res. 2015 May 1;14(5):1993-2001. doi: 10.1021/pr501138h. Epub 2015 Apr 22. Review.

3.

Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework.

Voillet V, Besse P, Liaubet L, San Cristobal M, González I.

BMC Bioinformatics. 2016 Oct 3;17(1):402.

4.

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 5;15:346. doi: 10.1186/s12859-014-0346-6.

5.

A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation.

Välikangas T, Suomi T, Elo LL.

Brief Bioinform. 2017 May 31. doi: 10.1093/bib/bbx054. [Epub ahead of print]

PMID:
28575146
6.

A nonparametric multiple imputation approach for missing categorical data.

Zhou M, He Y, Yu M, Hsu CH.

BMC Med Res Methodol. 2017 Jun 6;17(1):87. doi: 10.1186/s12874-017-0360-2.

7.

Normalization and missing value imputation for label-free LC-MS analysis.

Karpievitch YV, Dabney AR, Smith RD.

BMC Bioinformatics. 2012;13 Suppl 16:S5. doi: 10.1186/1471-2105-13-S16-S5. Epub 2012 Nov 5.

8.

SOMAscan™ used to investigate protein expression in non-small-cell lung cancer.

Hadjivasiliou AC.

Bioanalysis. 2012 Jun;4(10):1147. No abstract available.

PMID:
22826838
9.

Imputation of missing values of tumour stage in population-based cancer registration.

Eisemann N, Waldmann A, Katalinic A.

BMC Med Res Methodol. 2011 Sep 19;11:129. doi: 10.1186/1471-2288-11-129.

10.

On mining incomplete medical datasets: Ordering imputation and classification.

Chen CW, Lin WC, Ke SW, Tsai CF, Hu YH.

Technol Health Care. 2015;23(5):619-25. doi: 10.3233/THC-151018.

PMID:
26410122
11.

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 31;14(1):e1005973. doi: 10.1371/journal.pcbi.1005973. eCollection 2018 Jan.

12.

Multiple imputation and analysis for high-dimensional incomplete proteomics data.

Yin X, Levy D, Willinger C, Adourian A, Larson MG.

Stat Med. 2016 Apr 15;35(8):1315-26. doi: 10.1002/sim.6800. Epub 2015 Nov 12.

13.

Summarization vs Peptide-Based Models in Label-Free Quantitative Proteomics: Performance, Pitfalls, and Data Analysis Guidelines.

Goeminne LJ, Argentini A, Martens L, Clement L.

J Proteome Res. 2015 Jun 5;14(6):2457-65. doi: 10.1021/pr501223t. Epub 2015 May 7.

PMID:
25827922
14.

Tools for statistical analysis with missing data: application to a large medical database.

Preda C, Duhamel A, Picavet M, Kechadi T.

Stud Health Technol Inform. 2005;116:181-6.

PMID:
16160256
15.

Sequential imputation for missing values.

Verboven S, Branden KV, Goos P.

Comput Biol Chem. 2007 Oct;31(5-6):320-7. Epub 2007 Jul 10.

PMID:
17920334
16.

A statistical framework for protein quantitation in bottom-up MS-based proteomics.

Karpievitch Y, Stanley J, Taverner T, Huang J, Adkins JN, Ansong C, Heffron F, Metz TO, Qian WJ, Yoon H, Smith RD, Dabney AR.

Bioinformatics. 2009 Aug 15;25(16):2028-34. doi: 10.1093/bioinformatics/btp362. Epub 2009 Jun 17.

17.

A classifier ensemble approach for the missing feature problem.

Nanni L, Lumini A, Brahnam S.

Artif Intell Med. 2012 May;55(1):37-50. doi: 10.1016/j.artmed.2011.11.006. Epub 2011 Dec 20.

PMID:
22188722
18.

Missing data approaches in eHealth research: simulation study and a tutorial for nonmathematically inclined researchers.

Blankers M, Koeter MW, Schippers GM.

J Med Internet Res. 2010 Dec 19;12(5):e54. doi: 10.2196/jmir.1448.

19.

Proteomic patterns of tumour subsets in non-small-cell lung cancer.

Yanagisawa K, Shyr Y, Xu BJ, Massion PP, Larsen PH, White BC, Roberts JR, Edgerton M, Gonzalez A, Nadaf S, Moore JH, Caprioli RM, Carbone DP.

Lancet. 2003 Aug 9;362(9382):433-9.

PMID:
12927430
20.

Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets.

Huang MW, Lin WC, Tsai CF.

J Healthc Eng. 2018 Feb 4;2018:1817479. doi: 10.1155/2018/1817479. eCollection 2018.

Supplemental Content

Support Center