title: How advancement in biological network analysis methods empowers proteomics.
abstract: Proteomics provides important information--that may not be inferable from indirect sources such as RNA or DNA--on key players in biological systems or disease states. However, it suffers from coverage and consistency problems. The advent of network-based analysis methods can help in overcoming these problems but requires careful application and interpretation. This review considers briefly current trends in proteomics technologies and understanding the causes of critical issues that need to be addressed--i.e., incomplete data coverage and inter-sample inconsistency. On the coverage issue, we argue that holistic analysis based on biological networks provides a suitable background on which more robust models and interpretations can be built upon; and we introduce some recently developed approaches. On consistency, group-based approaches based on identified clusters, as well as on properly integrated pathway databases, are particularly useful. Despite that protein interactions and pathway networks are still largely incomplete, given proper quality checks, applications and reasonably sized data sets, they yield valuable insights that greatly complement data generated from quantitative proteomics.
authors.affiliation: Department of Computing, Imperial College London, UK.
authors.forename: Wilson W B
authors.initials: WW
authors.lastname: Goh
authors.forename: Yie H
authors.initials: YH
authors.lastname: Lee
authors.forename: Maxey
authors.initials: M
authors.lastname: Chung
authors.forename: Limsoon
authors.initials: L
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D053263:Gene Regulatory Networks; D013058:Mass Spectrometry / Q000379:methods; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D054730:Protein Interaction Domains and Motifs; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*
chemical_list: D011506:Proteins