Oliver Rinner

Molecular Biology · 2007–2015

A researcher in the Science corpus: 6 papers; trusts 3 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

Whom Oliver Rinner trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. John D. Storey
  2. Robert Tibshirani
  3. Trevor J. Hastie

Who trusts Oliver Rinner overall

  1. Juri Rappsilber
  2. Fengchao Yu

Papers from Oliver Rinner's perspective

Ordered by how strongly the researchers Oliver Rinner trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. (Analytical Chemistry)
  2. Statistical significance for genomewide studies (Proceedings of the National Academy of Sciences of the United States of America)
  3. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS (Proceedings of the National Academy of Sciences of the United States of America)
  4. Stable Isotope Labeling by Amino Acids in Cell Culture, SILAC, as a Simple and Accurate Approach to Expression Proteomics* (Molecular & Cellular Proteomics)
  5. A common open representation of mass spectrometry data and its application to proteomics research (Nature Biotechnology)
  6. Spectral Probabilities and Generating Functions of Tandem Mass Spectra: a Strike Against Decoy Databases (Journal of Proteome Research)
  7. Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis* (Molecular & Cellular Proteomics)
  8. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  9. mProphet: automated data processing and statistical validation for large-scale SRM experiments (Nature Methods)
  10. Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry* (Molecular & Cellular Proteomics)

Their papers