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  • team
  • department
  • center
  • program_project
  • nrc
  • whocc
  • project
  • software
  • tool
  • patent
  • Administrative Staff
  • Assistant Professor
  • Associate Professor
  • Clinical Research Assistant
  • Clinical Research Nurse
  • Clinician Researcher
  • Department Manager
  • Dual-education Student
  • Full Professor
  • Honorary Professor
  • Lab assistant
  • Master Student
  • Non-permanent Researcher
  • Nursing Staff
  • Permanent Researcher
  • Pharmacist
  • PhD Student
  • Physician
  • Post-doc
  • Prize
  • Project Manager
  • Research Associate
  • Research Engineer
  • Retired scientist
  • Technician
  • Undergraduate Student
  • Veterinary
  • Visiting Scientist
  • Deputy Director of Center
  • Deputy Director of Department
  • Deputy Director of National Reference Center
  • Deputy Head of Facility
  • Director of Center
  • Director of Department
  • Director of Institute
  • Director of National Reference Center
  • Group Leader
  • Head of Facility
  • Head of Operations
  • Head of Structure
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  • Labex Coordinator
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© Research
Publication : WIREs Data Mining and Knowledge Discovery

Mining proteomic data for biomedical research

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in WIREs Data Mining and Knowledge Discovery - 01 Jan 2012

Laura L. Elo, Benno Schwikowski

WIREs Data Mining Knowl Discov 2012, 2: 1–13 doi: 10.1002/widm.45

The popularity of proteomics in biomedical research has grown with the development of advanced measurement technologies. This has enabled high- throughput protein expression profiling, modification-specific proteomics, and global protein–protein interaction maps. Although proteomics has great potential in providing deeper understanding of the role of individual proteins and protein networks in disease and in unveiling the underlying disease mechanisms, challenges arise in transforming the large-scale experimental data into biomedical knowledge for clinical practice and drug development. In particular, sophisticated computational tools are required to interpret the high-dimensional proteomic datasets that typically reflect not only biological information, but also technical biases and limitations. This review gives an overview of the role of data mining in biomedical applications of proteomics, with a focus on data from mass spectrometry-based expression profiling studies.