Hyungwon Choi

Molecular Biology · 2008–2019

A researcher in the Science corpus: 7 papers; trusts 9 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

Whom Hyungwon Choi trusts overall

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

  1. Alexey I. Nesvizhskii
  2. Bradley Efron
  3. Michael A. Newton
  4. R. Aebersold
  5. B. Domon
  6. Jennifer Listgarten
  7. Shao-En Ong
  8. Andrew Emili
  9. Olga Vitek
  10. Christine Vogel

Who trusts Hyungwon Choi overall

  1. Karlie A. Neilson
  2. Guoci Teo
  3. Alexey I. Nesvizhskii

Papers from Hyungwon Choi's perspective

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

  1. Stable Isotope Labeling by Amino Acids in Cell Culture, SILAC, as a Simple and Accurate Approach to Expression Proteomics* (Molecular & Cellular Proteomics)
  2. Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry (Molecular & Cellular Proteomics)
  3. Analysis and validation of proteomic data generated by tandem mass spectrometry (Nature Methods)
  4. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. (Analytical Chemistry)
  5. Quantitative mass spectrometry in proteomics: a critical review (Analytical and Bioanalytical Chemistry)
  6. Interpretation of Shotgun Proteomic Data (Molecular & Cellular Proteomics)
  7. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics (Journal of Proteomics)
  8. 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)
  9. DAVID: Database for Annotation, Visualization, and Integrated Discovery (Genome Biology)
  10. mProphet: automated data processing and statistical validation for large-scale SRM experiments (Nature Methods)

Their papers