Bio-medical Ontologies Maintenance and Change Management

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VI Preface Chapter 2 proposes to intuitively organize into Resourceomes the hierarchical vision of a scientific domain perceived by its scientists, connecting the related resources to the topics they concern. In chapter 3, a small catalog of 74 selected genomic databases are analyzed and classified by considering both their biological contents and their technical features (e.g, how they may be queried, the database schemas they are based on, the different data formats, etc.). Chapter 4 considers the challenges of information integration in proteomics from the prospective of researchers using information technology as an integral part of their discovery process. Chapter 5 presents a series of aspects in the field of multimedia medical databases. These databases are the result of structuring the alphanumeric and imagistic data gathered in large quantities in the patient investigation and diagnosis processes. It also presents the necessity of creating and managing the multimedia medical databases and the advantages of using these operations in order to increase medical act efficiency. Chapter 6 surveys and reviews change management in bio-ontologies as well as some of the available tools and techniques in this area. It also surveys various potential changes in biomedical ontologies, with actual examples from some of the most popular ontologies in the biomedical domain. It investigates the potential of some of the advanced formalisms in this context by proposing formal method for analyzing and supporting ontology evolution and change management. In chapter 7 a framework for the application of data mining tools to constraint extraction in the biological domain is presented focusing on tuple constraint and functional dependency detection in representative biological databases by means of association rule mining. Chapter 8 proposes a method for classifying patterns in bioinformatics databases using Alpha-Beta Associative Memories. Chapter 9 described two pattern discovery analyses conducted on clinical data of patients in the follow-up of a liver transplantation. The two pattern discovery analyses used different techniques for different objectives. Chapter 10 presents a graph representation of metabolic pathways to describe all features of metabolic pathways and describes the application of graph-based relational learning for structure analysis on metabolic pathways in both supervised and unsupervised scenarios. In Chapter 11, a new Personal Health Office Framework (PhoF) is described which is designed to enable patients effectively manage their own. Chapter 12 focuses on the implementation of agent-based systems within the health domain, more specifically, in the study of total well-being. Chapter 13 evaluates the ability of the minimal model of glucose disappearance to describe experimental data collected from 9 diabetic patients controlled subcutaneously by an insulin pump. Lastly, chapter 14 presents the ab initio protein structure prediction as a conformational search problem in low resolution model using genetic algorithm. We hope this book will become a useful resource for bioinformatics graduate students, researchers, and practitioners interested in managing post-genome biology. We hope that bioinformatics students will use the book material as a guide to acquire basic concepts and theories of post-genome biomedical data management, bioinformatics practitioners will find valuable lessons for building future similar
Preface VII biomedical systems, and researchers will find rewarding research data management questions to address in years to come. April 2009 Perth, Australia Amandeep S. Sidhu Tharam S. Dillon
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A Summary of Genomic Databases: Ove
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Appendix A Summary of Genomic Datab
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Protein Data Integration Problem Am
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Fig. 3. Class Hierarchy of Protein
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Bio-medical Ontologies Maintenance
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TextToOnto (Maedche and Volz 2001)
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Extraction of Constraints from Biol
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Classifying Patterns in Bioinformat
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Mining Clinical, Immunological, and
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Substructure Analysis of Metabolic
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entry compound relation subtype com
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Running Time 4500 4000 3500 3000 25
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6 Conclusion Substructure Analysis
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Completing the Total Wellbeing Puzz
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Genetic Algorithm in Ab Initio Prot
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Author Index Apiletti, Daniele 169
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Bio-medical Ontologies Maintenance and Change Management

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