Year: 2008
Type: dissertation
Abstract: This thesis describes the development of text-mining \nalgorithms for molecular biology, in particular for DNA microarray \ndata analysis. Concept profiles were introduced, which characterize \nthe context in which a gene is mentioned in literature, to retrieve \nfunctional associations between genes. The method was shown to \nefficiently annotate DNA microarray data and complement existing \nmethods. Concept profiles were also used for other types of concepts \nand were successfully applied for functional annotation of genes \nthrough automatic assignment of Gene Ontology terms to genes. A \ngeneric framework has been developed based on concept profiles, dubbed \nAnni (www.biosemantics.org/anni), to provide researchers with an \nontology-based interface to the literature and we demonstrated its \nutility for literature-based knowledge discovery. Use and development \nof text-mining tools to identify relations between genes and to \nautomatically annotate sets of genes resulting from !\nmicroarray experiments.\nComparing DNA microarray studies can reveal interesting parallels. \nHowever, such analyses are hampered by the large influences of design, \ntechnical and statistical factors on the found differentially \nexpressed genes. Comparisons based on perturbed biological processes \ncould be more robust. Concept profiles were used to reveal overlapping \nbiological processes between microarray studies in a comparative meta- \nanalysis of 102 muscle-related microarray studies. We demonstrated \nthat many more biologically meaningful links could be retrieved \nbetween studies, even between studies without differentially expressed \ngenes in common.
Author: Rob Jelier
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2150485055