Mining significant high utility gene regulation sequential patterns

<p><strong>Background</strong></p> <p>Mining frequent gene regulation sequential patterns in time course microarray datasets is an important mining task in bioinformatics. Although finding such patterns are of paramount important for studying a disease, most existing work do not consider gene-disease association during gene regulation sequential pattern discovery. Moreover, they consider more absent/existence effects of genes during the mining process than taking the degrees of genes expression into account. Consequently, such techniques discover too many patterns which may not represent important information to biologists to investigate the relationships between the disease and underlying reasons hidden in gene regulation sequences.</p> <p><strong>Results</strong></p> <p>We propose a utility model by considering both the <em>gene-disease association</em> score and their <em>degrees of expression levels</em> under a biological investigation. We propose an efficient method called <em>Top-HUGS</em>, for discoverying <em>significant high utility gene regulation sequential patterns</em> from a time-course microarray dataset.</p> <p><strong>Conclusions</strong></p> <p>In this study, the proposed methods were evaluated on a publicly available time course microarray dataset. The experimental results show higher accuracies compared to the baseline methods. Our proposed methods found that several new gene regulation sequential patterns involved in such patterns were useful for biologists and provided further insights into the mechanisms underpinning biological processes. To effectively work with the proposed method, a web interface is developed to our system using Java. To the best of our knowledge, this is the first demonstration for <em>significant high utility gene regulation sequential pattern discovery</em>.</p>