Bioinformatics and data mining in proteomics

Abdelali Haoudi, Halima Bensmail

Research output: Contribution to journalReview article

31 Citations (Scopus)


Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.

Original languageEnglish
Pages (from-to)333-343
Number of pages11
JournalExpert Review of Proteomics
Issue number3
Publication statusPublished - 7 Jul 2006



  • Bioinformatics
  • Cancer
  • Classification
  • Clustering
  • Data mining
  • Infectious disease
  • Proteomics

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology

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