At the beginning of this century many sequencing projects were finalised. As a result, overwhelming amount of literature and sequence data have been available to biologist via online bioinformatics databases. This biological data lead to better understanding of many organisms and have helped identify genes. However, there is still much to learn about the functions and interactions of genes.
This thesis is concerned with predicting gene interactions using two main online resources: biomedical literature and sequence data. The biomedical literature is used to explore and refine a text mining method, known as the "co-occurrence method", which is used to predict gene interactions. The sequence data are used in an analysis to predict an upper bound of the number of genes involved in gene interactions.
The co-occurrence method of text mining was extensively explored in this thesis. The effects of certain computational parameters on influencing the relevancy of documents in which two genes co-occur were critically examined. The results showed that indeed some computational parameters do have an impact on the outcome of the co-occurrence method, and if taken into consideration, can lead to better identification of documents that describe gene interactions. To explore the co-occurrence method of text mining, a prototype system was developed, and as a result, it contains unique functions that are not present in currently available text mining systems.
Sequence data were used to predict the upper bound of the number of genes involved in gene interactions within a tissue. A novel approach was undertaken that used an analysis of SAGE and EST sequence libraries using ecological estimation methods. The approach proves that the species accumulation theory used in ecology can be applied to tag libraries (SAGE or EST) to predict an upper bound to the number of mRNA transcript species in a tissue.
The novel computational analysis provided in this study can be used to extend the body of knowledge and insights relating to gene interactions and, hence, provide better understanding of genes and their functions.
| Identifer | oai:union.ndltd.org:ADTP/211302 |
| Date | January 2008 |
| Creators | Dameh, Mustafa, n/a |
| Publisher | University of Otago. Department of Anatomy & Structural Biology |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://policy01.otago.ac.nz/policies/FMPro?-db=policies.fm&-format=viewpolicy.html&-lay=viewpolicy&-sortfield=Title&Type=Academic&-recid=33025&-find), Copyright Mustafa Dameh |
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