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Protein function in the post-genomic era

Author

Listed:
  • David Eisenberg

    (Molecular Biology Institute and UCLA-DOE Laboratory of Structural Biology and Molecular Medicine)

  • Edward M. Marcotte

    (Molecular Biology Institute and UCLA-DOE Laboratory of Structural Biology and Molecular Medicine)

  • Ioannis Xenarios

    (Molecular Biology Institute and UCLA-DOE Laboratory of Structural Biology and Molecular Medicine)

  • Todd O. Yeates

    (Molecular Biology Institute and UCLA-DOE Laboratory of Structural Biology and Molecular Medicine)

Abstract

Faced with the avalanche of genomic sequences and data on messenger RNA expression, biological scientists are confronting a frightening prospect: piles of information but only flakes of knowledge. How can the thousands of sequences being determined and deposited, and the thousands of expression profiles being generated by the new array methods, be synthesized into useful knowledge? What form will this knowledge take? These are questions being addressed by scientists in the field known as ‘functional genomics’.

Suggested Citation

  • David Eisenberg & Edward M. Marcotte & Ioannis Xenarios & Todd O. Yeates, 2000. "Protein function in the post-genomic era," Nature, Nature, vol. 405(6788), pages 823-826, June.
    • Handle: RePEc:nat:nature:v:405:y:2000:i:6788:d:10.1038_35015694
    DOI: 10.1038/35015694
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    Citations

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    Cited by:

    1. Blasi, Monica Francesca & Casorelli, Ida & Colosimo, Alfredo & Blasi, Francesco Simone & Bignami, Margherita & Giuliani, Alessandro, 2005. "A recursive network approach can identify constitutive regulatory circuits in gene expression data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 349-370.
    2. Jesse Gillis & Paul Pavlidis, 2011. "The Impact of Multifunctional Genes on "Guilt by Association" Analysis," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-16, February.
    3. Benedikt Boecking & Vincent Jeanselme & Artur Dubrawski, 2024. "Constrained clustering and multiple kernel learning without pairwise constraint relaxation," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(2), pages 309-324, June.
    4. Benjamin A Shoemaker & Anna R Panchenko, 2007. "Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners," PLOS Computational Biology, Public Library of Science, vol. 3(4), pages 1-7, April.
    5. Trendelina Rrustemi & Katrina Meyer & Yvette Roske & Bora Uyar & Altuna Akalin & Koshi Imami & Yasushi Ishihama & Oliver Daumke & Matthias Selbach, 2024. "Pathogenic mutations of human phosphorylation sites affect protein–protein interactions," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    6. Colizza, Vittoria & Flammini, Alessandro & Maritan, Amos & Vespignani, Alessandro, 2005. "Characterization and modeling of protein–protein interaction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 352(1), pages 1-27.
    7. Tracy Chih-Ting Koubkova-Yu & Jung-Chi Chao & Jun-Yi Leu, 2018. "Heterologous Hsp90 promotes phenotypic diversity through network evolution," PLOS Biology, Public Library of Science, vol. 16(11), pages 1-29, November.
    8. Lele Hu & Tao Huang & Xiaohe Shi & Wen-Cong Lu & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Predicting Functions of Proteins in Mouse Based on Weighted Protein-Protein Interaction Network and Protein Hybrid Properties," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-10, January.

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