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• W2015979730 endingPage "e25297" @default.
• W2015979730 startingPage "e25297" @default.
• W2015979730 abstract "Given a regulatory pathway system consisting of a set of proteins, can we predict which pathway class it belongs to? Such a problem is closely related to the biological function of the pathway in cells and hence is quite fundamental and essential in systems biology and proteomics. This is also an extremely difficult and challenging problem due to its complexity. To address this problem, a novel approach was developed that can be used to predict query pathways among the following six functional categories: (i) Metabolism, (ii) Genetic Information Processing, (iii) Environmental Information Processing, (iv) Cellular Processes, (v) Organismal Systems, and (vi) Human Diseases. The prediction method was established trough the following procedures: (i) according to the general form of pseudo amino acid composition (PseAAC), each of the pathways concerned is formulated as a 5570-D (dimensional) vector; (ii) each of components in the 5570-D vector was derived by a series of feature extractions from the pathway system according to its graphic property, biochemical and physicochemical property, as well as functional property; (iii) the minimum redundancy maximum relevance (mRMR) method was adopted to operate the prediction. A cross-validation by the jackknife test on a benchmark dataset consisting of 146 regulatory pathways indicated that an overall success rate of 78.8% was achieved by our method in identifying query pathways among the above six classes, indicating the outcome is quite promising and encouraging. To the best of our knowledge, the current study represents the first effort in attempting to identity the type of a pathway system or its biological function. It is anticipated that our report may stimulate a series of follow-up investigations in this new and challenging area." @default.
• W2015979730 created "2016-06-24" @default.
• W2015979730 creator A5025077602 @default.
• W2015979730 creator A5046736908 @default.
• W2015979730 creator A5076748700 @default.
• W2015979730 creator A5083281814 @default.
• W2015979730 date "2011-09-28" @default.
• W2015979730 modified "2023-10-09" @default.
• W2015979730 title "Classification and Analysis of Regulatory Pathways Using Graph Property, Biochemical and Physicochemical Property, and Functional Property" @default.
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• W2015979730 doi "https://doi.org/10.1371/journal.pone.0025297" @default.
• W2015979730 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3182212" @default.
• W2015979730 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/21980418" @default.
• W2015979730 hasPublicationYear "2011" @default.
• W2015979730 type Work @default.

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