Lineaarne mudel geeni ekspressiooni andmete analüüsi jaoks

Abstract

The thesis proposes a novel method for the analysis of microarray data based on fitting a specific linear model that combines microarray data with DNA sequence information. The model is both descriptive and predictive: its coefficients provide insight into the structure of the genetic regulatory networks, and its predictive performance may be used to find a set of genes that play important role in transcription regulation (transcription factors). An efficient algorithm is proposed for calculating the least-squares fit for the parameters of the model.

The proposed method is tested on a synthetic dataset and the results indicate that the approach is capable of detecting interesting relations in the data.