Haiyuan Yu

Molecular Biology · 2003–2018

A researcher in the Science corpus: 20 papers; trusts 47 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

Whom Haiyuan Yu trusts overall

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

  1. Mark Newman
  2. Gary D. Bader
  3. Michelle Girvan
  4. L. Kraal
  5. Ioannis Xenarios
  6. Chris Stark
  7. Duncan J. Watts
  8. Albert-László Barabási
  9. Lukasz Salwínski
  10. Stijn van Dongen

Who trusts Haiyuan Yu overall

  1. Trey Ideker
  2. Peter Csermely
  3. Ferenc Jorda¤n
  4. Giuseppe Gallone
  5. Roded Sharan
  6. Jishnu Das
  7. Damian Szklarczyk
  8. Michael E. Cusick
  9. Tamás Korcsmáros
  10. James C. Liao

Papers from Haiyuan Yu's perspective

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

  1. A comprehensive two-hybrid analysis to explore the yeast protein interactome (Proceedings of the National Academy of Sciences of the United States of America)
  2. The HUPO PSI's Molecular Interaction format—a community standard for the representation of protein interaction data (Nature Biotechnology)
  3. A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae (Nature)
  4. Comparative assessment of large-scale data sets of protein–protein interactions (Nature)
  5. Functional organization of the yeast proteome by systematic analysis of protein complexes (Nature)
  6. Collective dynamics of ‘small-world’ networks
  7. The Database of Interacting Proteins: 2004 update (Nucleic Acids Res.)
  8. Toward a protein-protein interaction map of the budding yeast: A comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins. (Proceedings of the National Academy of Sciences of the United States of America)
  9. Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae (Nature Genetics)
  10. Cytoscape: a software environment for integrated models of biomolecular interaction networks.

Their papers