A2 Refereed review article in a scientific journal
Mining proteomic data for biomedical research
Authors: Elo LL, Schwikowski B
Publisher: WILEY PERIODICALS, INC
Publication year: 2012
Journal: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Journal name in source: WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
Journal acronym: WIRES DATA MIN KNOWL
Number in series: 1
Volume: 2
Issue: 1
First page : 1
Last page: 13
Number of pages: 13
ISSN: 1942-4787
DOI: https://doi.org/10.1002/widm.45
Abstract
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. (C) 2011 Wiley Periodicals, Inc.
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. (C) 2011 Wiley Periodicals, Inc.
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