Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies

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Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies

Lazar, C., Gatto, L., Ferro, M., Bruley, C., and Burger, T. (2016): Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies. Journal of Proteome Research, 15:1116–1125.

https://doi.org/10.1021/acs.jproteome.5b00981


Summary

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Study outcomes

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Outcome O1

The performance of ...

Outcome O1 is presented as Figure X in the original publication.

Outcome O2

...

Outcome O2 is presented as Figure X in the original publication.

Outcome On

...

Outcome On is presented as Figure X in the original publication.

Further outcomes

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Study design and evidence level

General aspects

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Design for Outcome O1

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  • Configuration parameters were chosen ...
  • ...

Design for Outcome O2

  • The outcome was generated for ...
  • Configuration parameters were chosen ...
  • ...

...

Design for Outcome O

  • The outcome was generated for ...
  • Configuration parameters were chosen ...
  • ...

Further comments and aspects

References

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