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Authors: Giuseppe Profiti ; Damiano Piovesan ; Pier Luigi Martelli ; Piero Fariselli and Rita Casadio

Affiliation: University of Bologna, Italy

ISBN: 978-989-8565-35-8

Keyword(s): Graphs, Community Detection, Protein Sequences, Automated Annotation.

Related Ontology Subjects/Areas/Topics: Algorithms and Software Tools ; Bioinformatics ; Biomedical Engineering ; Genomics and Proteomics ; Pattern Recognition, Clustering and Classification ; Sequence Analysis

Abstract: Given the exponentially increasing amount of available data, electronic annotation procedures for protein sequences are a core topic in bioinformatics. In this paper we present the refinement of an already published procedure that allows a fine grained level of detail in the annotation results. This enhancement is based on a graph representation of the similarity relationship between sequences within a cluster, followed by the application of community detection algorithms. These algorithms identify groups of highly connected nodes inside a bigger graph. The core idea is that sequences belonging to the same community share more features in respect to all the other sequences in the same graph.

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Paper citation in several formats:
Profiti, G.; Piovesan, D.; Luigi Martelli, P.; Fariselli, P. and Casadio, R. (2013). Community Detection within Clusters Helps Large Scale Protein Annotation - Preliminary Results of Modularity Maximization for the BAR+ Database.In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013) ISBN 978-989-8565-35-8, pages 328-332. DOI: 10.5220/0004328703280332

@conference{bioinformatics13,
author={Giuseppe Profiti. and Damiano Piovesan. and Pier Luigi Martelli. and Piero Fariselli. and Rita Casadio.},
title={Community Detection within Clusters Helps Large Scale Protein Annotation - Preliminary Results of Modularity Maximization for the BAR+ Database},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013)},
year={2013},
pages={328-332},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004328703280332},
isbn={978-989-8565-35-8},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013)
TI - Community Detection within Clusters Helps Large Scale Protein Annotation - Preliminary Results of Modularity Maximization for the BAR+ Database
SN - 978-989-8565-35-8
AU - Profiti, G.
AU - Piovesan, D.
AU - Luigi Martelli, P.
AU - Fariselli, P.
AU - Casadio, R.
PY - 2013
SP - 328
EP - 332
DO - 10.5220/0004328703280332

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