An inductive logic programming approach to learning which uORFs regulate gene expression.
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Some upstream open reading frames (uORFs) regulate gene expression (i.e. they are functional) and can play key roles in keeping organisms healthy. However, how uORFs are involved in gene regulation is not het fully understood. In order to get a complete view of how uORFs are involved in gene regulation, it is expected that a large number of functional uORFs are needed. Unfortunately , lab experiments to verify that uORFs are functional are expensive. In this thesis, for the first time, the use of inductive logic programming (ILP) is explored for the task of learning which uORFs regulate gene expression in the yeast Saccharomyces cerevisiae. This work is directed to help select sets of candidate functional uORFs for experimental studies. With limited background knowledge, ILP can generate hypotheses which make the search for novel functional uORFs 17 times more efficient than random sampling. Adding mRNA secondary structure to the background knowledge results in hypotheses with significantly increased performance. This work is the first machine learning work to study both uORFs and mRNA secondary structures in the context of gene regulation. Using a novel combination of knowledge about biological conservation, gene ontology annotations and genes' response to different conditions results in hypotheses that are simple, informative, have an estimated sensitivity of 81% and provide provisional insights into biological characteristics of functional uORFs. The hypotheses predict 299 further genes to have 450 novel functional uORFs. A comparison with a related study suggests that 8 of these predicted functional uORFs (from 8 genes) are strong candidates for experimental studies.