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Application of fuzzy clustering in analysis of included proteins in esophagus, stomach and colon cancers based on similarity of Gene Ontology annotation

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Author(s): Yalda Zarnegarnia | Hamid Alavi Majd | Mostafa Rezaei Tavirani | Nasibe Khaier | Ali akbar Khadem Maboodi

Journal: Koomesh
ISSN 1608-7046

Volume: 12;
Issue: 01;
Start page: 14;
Date: 2010;
Original page

Keywords: Bioinformatics | Gene Ontology annotation | Fuzzy clustering | Gastric system cancer

ABSTRACT
Introduction: Because of producing large amount of proteomics data and requiring new proceduresfor analyzing them, collective analysis of proteins can help us in identifying new annotation patterns indataset. Furthermore, this type of analysis is a time- consuming process too. Cluster analysis, as a suitablestatistic procedure, can be used for analyzing these datasets. This paper's objective was evaluating theefficiency of fuzzy clustering method in recognizing new patterns within proteins which are related togastric cancers.Materials and Methods: Fuzzy clustering procedure has been used to analyze the identified includedproteins in esophagus, stomach and colon cancers. Proteins were clustered based on three aspects of GeneOntology (GO) and results were compared.Results: Fuzzy clustering was implemented and non-fuzziness indexes based on biological process,cellular component and molecular function were obtained equal to 0.41, 0.55 and 0.35, respectively.Obtained index based on molecular function showed the efficiency of fuzzy clustering method. Despite ofnon-substantial silhouette widths for the entire dataset, most of the proteins in each cluster hadremarkable biological communions. Using Term Enrichment software to determine statistically enrichedGO terms in the entire dataset and clusters, it was cleared that the fuzzy clustering has revealed novelannotation patterns within dataset that would not have been identified otherwise.Conclusion: Considering fuzzy clustering outputs, the efficiency of this method for better and flexibleproteins analysis was cleared. As fuzzy clustering method has placed proteins, that have more similarities,with high probabilities together. Therefore, it can be used for the situations that some of proteins haveunknown characteristics. Furthermore it seems that the proteins clustered via their cellular componentsimilarities, have also biological and functional similarities which this requires more investigations.

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Tangokurs Rapperswil-Jona

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