DIMA: Data-Driven Selection of an Imputation Algorithm-Reference-Cited by-同舟云学术

DIMA: Data-Driven Selection of an Imputation Algorithm

Published:2021-06-01 Issue:7 Volume:20 Page:3489-3496
ISSN:1535-3893
Container-title:Journal of Proteome Research
language:en
Short-container-title:J. Proteome Res.

Author:

Egert Janine¹²^ORCID,Brombacher Eva¹²³⁴,Warscheid Bettina⁵⁶^ORCID,Kreutz Clemens¹⁶⁷

Affiliation:

1. Institute of Medical Biometry and Statistics (IMBI), Institute of Medicine and Medical Center Freiburg, 79104 Freiburg im Breisgau, Germany

2. Centre for Integrative Biological Signalling Studies (CIBSS), Albert-Ludwigs-Universität Freiburg, 79104 Freiburg, Germany

3. Spemann Graduate School of Biology and Medicine (SGBM), Albert-Ludwigs-Universität Freiburg, 79104 Freiburg, Germany

4. Faculty of Biology, Albert-Ludwigs-Universität Freiburg, 79104 Freiburg im Breisgau, Germany

5. Biochemistry and Functional Proteomics, Institute of Biology II, Faculty of Biology, Albert-Ludwigs-Universität Freiburg, 79104 Freiburg im Breisgau, Germany

6. Signalling Research Centres BIOSS and CIBSS, Albert-Ludwigs-Universität Freiburg, 79104 Freiburg im Breisgau, Germany

7. Center for Data Analysis and Modeling (FDM), Albert-Ludwigs-Universität Freiburg, 79104 Freiburg im Breisgau, Germany

Funder

Bundesministerium für Bildung und Forschung

Deutsche Forschungsgemeinschaft

H2020 Marie Sklodowska-Curie Actions

H2020 European Research Council

Publisher

American Chemical Society (ACS)

Subject

General Chemistry,Biochemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jproteome.1c00119

Reference38 articles.

1. The use of missing values in proteomic data-independent acquisition mass spectrometry to enable disease activity discrimination

2. Strategies to enable large-scale proteomics for reproducible research

3. Multibatch TMT Reveals False Positives, Batch Effects and Missing Values

4. Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data

5. Review, Evaluation, and Discussion of the Challenges of Missing Value Imputation for Mass Spectrometry-Based Label-Free Global Proteomics

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