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Prediction of Human Genes' Regulatory Functions Based on Proteinprotein Interaction Network

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Abstract:

In systems biology, regulatory pathway is one of the most important research areas. However, regulatory pathway is so complicated that we still poorly understand this system. On the other hand, with rapid accumulated information on different organisms, it becomes more and more possible to in-depth investigate regulatory pathway. To understand regulatory pathway well, figuring out the components of each pathway is the most important step. In this study, a network- based method was proposed to classify human genes into corresponding pathways. The information of proteinprotein interactions retrieved from STRING was used to construct a network and jackknife test was employed to evaluate the method. As a result, the first order prediction accuracy was 87.91%, indicating that interactive proteins always have similar biological regulatory functions. By comparing the predicted results obtained from other methods based on blast and amino acid composition, respectively, it implies that our prediction method is quite promising that may provide an opportunity to understand this complicated pathway system well.

Keywords: KEGG; Regulatory pathway; STRING; jackknife cross-validation; network-based method; protein-protein interaction

Document Type: Research Article

DOI: http://dx.doi.org/10.2174/092986612802084528

Publication date: September 1, 2012

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.

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