Grouping Biological Data

Today, scientists in biomedical fields rely on biological data sources in their research. Large amounts of information concerning genes, proteins and diseases are available on the internet, and are used daily for acquiring knowledge. Typically, biological data is spread across multiple sources, which has led to heterogeneity and redundancy. The current thesis suggests grouping as one way of computationally managing biological data. A conceptual model for this purpose is presented, which takes properties specific for biological data into account. The model defines sub-tasks and key issues where multiple solutions are possible, and describes what approaches that have been used in earlier work. Further, an implementation of this model is described, as well as test cases which show that the model is indeed useful. Since the use of ontologies is relatively new in the management of biological data, the main focus of the thesis is on how semantic similarity of ontological annotations can be used for grouping. The results of the test cases show for example that the implementation of the model, using Gene Ontology, is capable of producing groups of data entries with similar molecular functions

Autorentext

David Rundqvist is a Master Graduate of Bioinformatics, a broadprofile combining mathematics, computer science and biology. Thepresent thesis offers a strategy that aims at reducing redundancyin biological databases. The paper has received the award "BestGraduation Thesis 2006" at the department of Computer Sciences atLinköping University.

Klappentext

Today, scientists in biomedical elds rely on biological data sources in their research. Large amounts of information concerning genes, proteins and diseases are available on the internet, and are used daily for acquiring knowledge. Typically, biological data is spread across multiple sources, which has led to heterogeneity and redundancy. The current thesis suggests grouping as one way of computationally managing biological data. A conceptual model for this purpose is presented, which takes properties speci c for biological data into account. The model de nes sub-tasks and key issues where multiple solutions are possible, and describes what approaches that have been used in earlier work. Further, an implementation of this model is described, as well as test cases which show that the model is indeed useful. Since the use of ontologies is relatively new in the management of biological data, the main focus of the thesis is on how semantic similarity of ontological annotations can be used for grouping. The results of the test cases show for example that the implementation of the model, using Gene Ontology, is capable of producing groups of data entries with similar molecular functions