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Towards the assessment of semantic similarity analysis of protein data: main approaches and issues

Towards the assessment of semantic similarity analysis of protein data: main approaches and issues Towards the Assessment of Semantic Similarity Analysis of Protein Data: Main Approaches and Issues Pietro Hiram Guzzi University of Catanzaro Marco Mina University of Padova ABSTRACT Bioinformatics approaches to the study of proteins yield to the introduction of different methodologies and related tools for the analysis of different types of data related to proteins, ranging from primary, secondary and tertiary structures to interaction data [1], not to mention functional knowledge. One of the most advanced tools for encoding and representing functional knowledge in a formal way is the Gene Ontology (GO) [2,3]. It is composed of three ontologies, named Biological Process (BP), Molecular Function (MF) and Cellular Component (CC). Each ontology consists of a set of terms (GO terms) representing different functions, biological processes and cellular components within the cell. GO terms are connected each other to form a hierarchical graph. Terms representing similar functions are close to each other within this graph. Biological molecules are associated with GO terms that represent their functions, biological roles and localization. This process, usually referred to as annotation process, can be performed under the supervision of an expert or in a fully automated way. Obviously, computationally inferred annotations, commonly known as http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGBioinformatics Record Association for Computing Machinery

Towards the assessment of semantic similarity analysis of protein data: main approaches and issues

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2012 by ACM Inc.
ISSN
2331-9291
DOI
10.1145/2384691.2384694
Publisher site
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Abstract

Towards the Assessment of Semantic Similarity Analysis of Protein Data: Main Approaches and Issues Pietro Hiram Guzzi University of Catanzaro Marco Mina University of Padova ABSTRACT Bioinformatics approaches to the study of proteins yield to the introduction of different methodologies and related tools for the analysis of different types of data related to proteins, ranging from primary, secondary and tertiary structures to interaction data [1], not to mention functional knowledge. One of the most advanced tools for encoding and representing functional knowledge in a formal way is the Gene Ontology (GO) [2,3]. It is composed of three ontologies, named Biological Process (BP), Molecular Function (MF) and Cellular Component (CC). Each ontology consists of a set of terms (GO terms) representing different functions, biological processes and cellular components within the cell. GO terms are connected each other to form a hierarchical graph. Terms representing similar functions are close to each other within this graph. Biological molecules are associated with GO terms that represent their functions, biological roles and localization. This process, usually referred to as annotation process, can be performed under the supervision of an expert or in a fully automated way. Obviously, computationally inferred annotations, commonly known as

Journal

ACM SIGBioinformatics Record – Association for Computing Machinery

Published: Sep 1, 2012

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