Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation-Reference-Cited by-同舟云学术

Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation

Published:2024-08-27 Issue:9 Volume:23 Page:4151-4162
ISSN:1535-3893
Container-title:Journal of Proteome Research
language:en
Short-container-title:J. Proteome Res.

Author:

Yan Yu¹²³^ORCID,Sankar Baradwaj Simha¹³,Mirza Bilal¹²,Ng Dominic C. M.¹²³,Pelletier Alexander R.²⁴,Huang Sarah D.¹²^ORCID,Wang Wei²⁴,Watson Karol¹³,Wang Ding¹²³,Ping Peipei¹²³⁴

Affiliation:

1. Departments of Physiology and Medicine, University of California, Los Angeles (UCLA) School of Medicine, Los Angeles, California 90095, United States

2. NHLBI Integrated Cardiovascular Data Science Training Program, UCLA, Los Angeles, California 90095, United States

3. NIH BRIDGE2AI Center & NHLBI Integrated Cardiovascular Data Science Training Program, UCLA, Suite 1-609, MRL Building, 675 Charles E. Young Drive South, Los Angeles, California 90095, United States

4. Department of Computer Science and Scalable Analytics Institute, UCLA School of Engineering, Los Angeles, California 90095, United States

Funder

University of California, Los Angeles

National Heart, Lung, and Blood Institute

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jproteome.4c00263

Reference42 articles.

1. Robust Score Tests With Missing Data in Genomics Studies

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

3. Imputation of missing values for electronic health record laboratory data

4. Data Missing Not at Random in Mobile Health Research: Assessment of the Problem and a Case for Sensitivity Analyses

5. A comparative study of evaluating missing value imputation methods in label-free proteomics