Computational Pipeline Optimization
Computational Pipeline Optimization is a methodological framework within label-free quantification that systematically enhances the efficiency, accuracy, and reproducibility of computational workflows by refining algorithmic parameters, data processing steps, and resource allocation strategies specific to mass spectrometry-based proteomic analyses.
34
Publications
1.8K
Citations
222
Authors
112
Institutions
Publications per year
2017–2026
24
Parents
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EASI-FISH for thick tissue defines lateral hypothalamus spatio-molecular organization
Yuhan Wang, Mark Eddison, Greg Fleishman et al. · Cell · 2021 · 210 citations · Full text
Le Shu, Yuqi Zhao, Zeyneb Kurt et al. · BMC Genomics · 2016 · 134 citations · Full text
A computational pipeline for comparative ChIP-seq analyses
Anaïs F. Bardet, Qiye He, Julia Zeitlinger et al. · Nature Protocols · 2011 · 123 citations
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112

Chinese Academy of Sciences
Beijing, China
2 h-index
7 publications
113 citations

University of Manchester
Manchester, United Kingdom
2 h-index
6 publications
190 citations
Heidelberg University
Heidelberg, Germany
2 h-index
6 publications
338 citations
University of California San Diego
San Diego, United States
2 h-index
2 publications
246 citations
University of Chicago
Chicago, United States
2 h-index
6 publications
766 citations
University of California, Los Angeles
Los Angeles, United States
2 h-index
10 publications
983 citations
Health Data Research UK
London, United Kingdom
1 h-index
1 publications
62 citations
The University of Texas MD Anderson Cancer Center
Houston, United States
1 h-index
2 publications
22 citations
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2 publications · 152 citations
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2 publications · 145 citations
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TOG · 2 publications · 62 citations
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2 publications · 82 citations
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1 publications · 65 citations
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