Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics
Authors
Keywords
-
Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 22, Issue 17, Pages 9650
Publisher
MDPI AG
Online
2021-09-07
DOI
10.3390/ijms22179650
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Fibroblast-Specific Proteo-Transcriptomes Reveal Distinct Fibrotic Signatures of Human Sinoatrial Node in Non-Failing and Failing Hearts
- (2021) Anuradha Kalyanasundaram et al. CIRCULATION
- Comparative evaluation of label-free quantification strategies
- (2020) Lei Zhao et al. Journal of Proteomics
- Tagging enhances histochemical and biochemical detection of Ran Binding Protein 9 in vivo and reveals its interaction with Nucleolin
- (2020) Shimaa H. A. Soliman et al. Scientific Reports
- Proper imputation of missing values in proteomics datasets for differential expression analysis
- (2020) Mingyi Liu et al. BRIEFINGS IN BIOINFORMATICS
- MSnbase, Efficient and Elegant R-Based Processing and Visualization of Raw Mass Spectrometry Data
- (2020) Laurent Gatto et al. JOURNAL OF PROTEOME RESEARCH
- A microfluidic chip enables isolation of exosomes and establishment of their protein profiles and associated signaling pathways in ovarian cancer
- (2019) Kalpana Deepa Priya Dorayappan et al. CANCER RESEARCH
- GMSimpute: a generalized two-step Lasso approach to impute missing values in label-free mass spectrum analysis
- (2019) Qian Li et al. BIOINFORMATICS
- Advances in Current Diabetes Proteomics: From the Perspectives of Label-free Quantification and Biomarker Selection
- (2019) Jianbo Fu et al. CURRENT DRUG TARGETS
- The long non-coding RNA HOXB-AS3 regulates ribosomal RNA transcription in NPM1-mutated acute myeloid leukemia
- (2019) Dimitrios Papaioannou et al. Nature Communications
- Refinements of LC-MS/MS Spectral Counting Statistics Improve Quantification of Low Abundance Proteins
- (2019) Ha Yun Lee et al. Scientific Reports
- PANDA-view: an easy-to-use tool for statistical analysis and visualization of quantitative proteomics data
- (2018) Cheng Chang et al. BIOINFORMATICS
- GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies
- (2018) Runmin Wei et al. PLoS Computational Biology
- statTarget: A streamlined tool for signal drift correction and interpretations of quantitative mass spectrometry-based omics data
- (2018) Hemi Luan et al. ANALYTICA CHIMICA ACTA
- Learning and Imputation for Mass-spec Bias Reduction (LIMBR)
- (2018) Alexander M Crowell et al. BIOINFORMATICS
- Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data
- (2018) Runmin Wei et al. Scientific Reports
- The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments
- (2018) Jonathon J. O’Brien et al. Annals of Applied Statistics
- Integrated identification and quantification error probabilities for shotgun proteomics
- (2018) Matthew The et al. MOLECULAR & CELLULAR PROTEOMICS
- Proteogenomic integration reveals therapeutic targets in breast cancer xenografts
- (2017) Kuan-lin Huang et al. Nature Communications
- In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values
- (2017) Jinxia Wang et al. Scientific Reports
- DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics
- (2016) Samuel Wieczorek et al. BIOINFORMATICS
- Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework
- (2016) Valentin Voillet et al. BMC BIOINFORMATICS
- Tag-Count Analysis of Large-Scale Proteomic Data
- (2016) Owen E. Branson et al. JOURNAL OF PROTEOME RESEARCH
- Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies
- (2016) Cosmin Lazar et al. JOURNAL OF PROTEOME RESEARCH
- A multi-model statistical approach for proteomic spectral count quantitation
- (2016) Owen E. Branson et al. Journal of Proteomics
- Dynamic Protein Interactions of the Polycomb Repressive Complex 2 during Differentiation of Pluripotent Cells
- (2016) Giorgio Oliviero et al. MOLECULAR & CELLULAR PROTEOMICS
- The Perseus computational platform for comprehensive analysis of (prote)omics data
- (2016) Stefka Tyanova et al. NATURE METHODS
- Review, Evaluation, and Discussion of the Challenges of Missing Value Imputation for Mass Spectrometry-Based Label-Free Global Proteomics
- (2015) Bobbie-Jo M. Webb-Robertson et al. JOURNAL OF PROTEOME RESEARCH
- Multiple imputation and analysis for high-dimensional incomplete proteomics data
- (2015) Xiaoyan Yin et al. STATISTICS IN MEDICINE
- The Q Exactive HF, a Benchtop Mass Spectrometer with a Pre-filter, High-performance Quadrupole and an Ultra-high-field Orbitrap Analyzer
- (2014) Richard Alexander Scheltema et al. MOLECULAR & CELLULAR PROTEOMICS
- Normalization and missing value imputation for label-free LC-MS analysis
- (2012) Yuliya V Karpievitch et al. BMC BIOINFORMATICS
- MSnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation
- (2011) Laurent Gatto et al. BIOINFORMATICS
- Discovery of Mouse Spleen Signaling Responses to Anthrax using Label-Free Quantitative Phosphoproteomics via Mass Spectrometry
- (2010) Nathan P. Manes et al. MOLECULAR & CELLULAR PROTEOMICS
- A statistical framework for protein quantitation in bottom-up MS-based proteomics
- (2009) Yuliya Karpievitch et al. BIOINFORMATICS
- Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition
- (2009) Yuliya V. Karpievitch et al. BIOINFORMATICS
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started