Detecting influential transcription factors using linear models
Name
Nikita Shipilov
Abstract
With the recent development of the high throughput DNA microarray technology, it became possible to measure the levels of gene activity on a large scale. The data collected from a microarray usually requires sophisticated analysis involving biological knowledge and the application of statistical techniques.
In this work the problem of inferring ‘influential’ transcription factors from microarray data using linear models is addressed. Linear models are easy to understand and are able to produce interpretable solutions. The state-of-the-art methods for solving linear regression problems and their applicability to biological data are described in the paper.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Konstantin Tretjakov
Defence year
2010