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Related Concept Videos

Proteomics01:33

Proteomics

10.2K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
10.2K

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Related Experiment Video

Updated: Mar 30, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Published on: November 15, 2017

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

Xiaoyan Yin1,2,3, Daniel Levy1,4, Christine Willinger1,4

  • 1The Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA, U.S.A.

Statistics in Medicine
|November 14, 2015
PubMed
Summary
This summary is machine-generated.

Multiple imputation effectively handles missing proteomics data in myocardial infarction studies. This method identified seven key plasma proteins associated with the condition, improving multivariable analysis.

Keywords:
high dimensionimputation qualitymultiple imputationstepwise selection

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Area of Science:

  • Cardiovascular Disease Research
  • Proteomics and Bioinformatics
  • Statistical Genetics

Background:

  • Multivariable analysis of proteomics data is often limited by missing values.
  • Nested case-control studies require robust methods to handle incomplete high-dimensional data.
  • The Framingham Heart Study Offspring cohort provided data for this myocardial infarction investigation.

Purpose of the Study:

  • To evaluate the feasibility of multiple imputation for analyzing incomplete proteomics data in a myocardial infarction case-control study.
  • To develop and optimize a multiple imputation strategy for high-dimensional proteomics datasets with missing values.
  • To identify plasma protein biomarkers associated with myocardial infarction using imputed data.

Main Methods:

  • A nested case-control study design was employed using 135 myocardial infarction cases and 135 matched controls.
  • Multiple imputation techniques were explored and optimized using simulation with complete and artificially masked proteomics data.
  • Conditional logistic regression was used for stepwise variable selection on imputed datasets, with proteins binned for imputation.
  • The optimized method was applied to 544 proteins with up to 40% missing values.

Main Results:

  • Multiple imputation was found to be a feasible approach for handling missing proteomics data in this cohort.
  • An optimal multiple imputation strategy was determined through simulation, considering imputation methods, bin sizes, and shuffling iterations.
  • A panel of seven plasma proteins was identified as being jointly associated with myocardial infarction risk.

Conclusions:

  • Multiple imputation is a viable strategy to overcome challenges posed by missing data in multivariable proteomics analyses for cardiovascular disease.
  • The identified seven-protein panel may serve as potential biomarkers for myocardial infarction.
  • This approach enhances the utility of large-scale proteomics datasets in epidemiological studies.