Predicting genetic regulatory response using classification
Bioinformatics
Inductive logic programming for gene regulation prediction
Machine Learning
Top-down induction of first-order logical decision trees
Artificial Intelligence
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One of the central goals in computational and systems biology is to understand the mechanisms of gene transcriptional regulation on a system-wide level. The efforts are often based on high-throughput genomic data of model organisms such as S. cerevisiae. The goal of this work is to learn a model of gene regulation predicting under which conditions genes are up- or down-regulated. Our starting point is the model of Middendorf et al.[1], where the presence of transcription factor binding sites (motifs) in the gene's regulatory region and the expression levels of regulators (e.g., transcription factors or protein kinases) are used to predict gene regulation. It is clear that in this formulation, important information related to gene regulation is missing, for instance due to post-translational modifications. Thus, information integration could be extremely useful to fill in and take into account various missing pieces of information related to gene regulation.