Since the initial genome sequencing projects along with the recent advances on technology,
molecular biology and large scale transcriptome analysis result in data accumulation
at a large scale. These data have been provided in different platforms and come from
different laboratories therefore, there is a need for compilation and comprehensive analysis.
In this thesis, we addressed the automatization of biological data acquisition and
integration from these non-uniform data using machine learning techniques. We focused
on two different mining studies in the scope of this thesis. In the first study, we worked on
characterizing expression patterns of housekeeping genes. We described methodologies
to compare measures of housekeeping genes with non-housekeeping genes. In the second
study, we proposed a novel framework, bi-k-bi clustering, for finding association rules of
gene pairs that can easily operate on large scale and multiple heterogeneous data sets.
Results in both studies showed consistency and relatedness with the available literature.
Furthermore, our results provided some novel insights waiting to be experimented by the
biologists.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12610396/index.pdf |
| Date | 01 February 2009 |
| Creators | Carkacioglu, Levent |
| Contributors | Atalay, Volkan |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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