Article Abstract

	Title:	Modelling concepts and database implementation techniques for complex biological data
	Author:	Ramez Elmasri, Feng Ji, Jack Fu, Yiming Zhang, Zoe Raja
	Address:	Department of Computer Science and Engineering, University of Texas at Arlington, P.O. Box 19015, Arlington, TX 76019, USA. ' Department of Computer Science and Engineering, University of Texas at Arlington, P.O. Box 19015, Arlington, TX 76019, USA. ' Department of Computer Science and Engineering, University of Texas at Arlington, P.O. Box 19015, Arlington, TX 76019, USA. ' Department of Computer Science and Engineering, University of Texas at Arlington, P.O. Box 19015, Arlington, TX 76019, USA. ' Department of Computer Science and Engineering, University of Texas at Arlington, P.O. Box 19015, Arlington, TX 76019, USA
	Journal:	International Journal of Bioinformatics Research and Applications 2007 - Vol. 3, No.3  pp. 366 - 388
	Abstract:	Biological data such as protein structure and function, DNA sequences, and metabolic pathways require conceptual modelling characteristics that are not available in the widely used Entity-Relationship (ER) model and its variants, such as the Enhanced-Entity Relationship (EER) model. In particular, three constructs that occur frequently in bioinformatics data are ordered relationships, functional processes, and 3-dimensional (3D) structures. In this paper, we suggest a solution to this problem, requiring only minimal changes to the EER model by introducing specialised formal relationships for ordering, processes and molecular spatial structure. We show how these new concepts can be implemented in relational databases.
	Keywords:	bioinformatics data; entity relationship modelling; EER models; ontology; relational databases; enhanced-entity relationship; ordered relationships; functional processes; molecular spatial structure.
	DOI:	10.1504/IJBRA.2007.015008
		Purchase this Paper         Comment on the Paper