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Mining proteomic data for biomedical research
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DOI：10.1002/widm.45.png)
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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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The HUPO proteomics standards initiativeeasing communication and minimizing data loss in a changing worldHUPO蛋白质组学标准在不断变化的世界中启动通信并最大程度地减少数据丢失
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