Analyzing proteins from single cells by tandem mass spectrometry (MS) has recently
become technically feasible. While such analysis has the potential to accurately quantify …

Proteomics data are often plagued with missingness issues. These missing values (MVs)
threaten the integrity of subsequent statistical analyses by reduction of statistical power …

Missing values are a notable challenge when analyzing mass spectrometry-based
proteomics data. While the field is still actively debating the best practices, the challenge …

Quantitative measurements produced by tandem mass spectrometry proteomics
experiments typically contain a large proportion of missing values. Missing values hinder …

Recent surges in large-scale mass spectrometry (MS)-based proteomics studies demand a
concurrent rise in methods to facilitate reliable and reproducible data analysis …

Missing values (MVs) can adversely impact data analysis and machine-learning model
development. We propose a novel mixed-model method for missing value imputation (MVI) …

Missing values are a major issue in quantitative proteomics analysis. While many methods
have been developed for imputing missing values in high-throughput proteomics data, a …

Spatially targeted proteomics analyzes the proteome of specific cell types and functional
regions within tissue. While spatial context is often essential to understanding biological …

The work presented in this paper aims to develop new imputation methods to better handle
missing values encountered in astronomical data analysis, especially the classification of …

With advancements in modern medical technology, an increasing amount of cancer-related
information can be acquired through various means, such as genomics, proteomics …