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J Theor Biol. 2014 Dec 7;362:44-52. doi: 10.1016/j.jtbi.2014.05.031. Epub 2014 Jun 6.

Pathway and network analysis in proteomics.

Author information

1
Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, Wenzhou, Zhejiang Province, China; School of Informatics and Computing, Indiana University-Purdue University, Indianapolis, IN 46202, USA; Institute for Systems Biology, Seattle, WA 98109, USA.
2
Department of Computer Science and Information Science Purdue University, Indianapolis, IN 46202, USA.
3
Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, Wenzhou, Zhejiang Province, China; School of Informatics and Computing, Indiana University-Purdue University, Indianapolis, IN 46202, USA; Department of Computer Science and Information Science Purdue University, Indianapolis, IN 46202, USA; Indiana Center for Systems Biology and Personalized Medicine, Indiana University, Indianapolis, IN 46202, USA. Electronic address: jakechen@iupui.edu.

Abstract

Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics.

KEYWORDS:

Complex networks; Functional analysis; Hybrid strategy; Network modules; Pathway analysis

PMID:
24911777
PMCID:
PMC4253643
DOI:
10.1016/j.jtbi.2014.05.031
[Indexed for MEDLINE]
Free PMC Article

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