Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference
Identification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step...
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| Main Authors: | , , , , |
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| 格式: | Article |
| 語言: | English |
| 出版: |
2024
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| 在線閱讀: | https://hdl.handle.net/10356/178809 |
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