Finding discriminative motifs has recently received much attention in biomedical field as such motifs allows us to characterize in distinguishing two different classes of sequences. Although the developed methods function on labeled data, it is common in biomedical applications that the quantity of labeled sequences is limited while a large number of unlabeled sequences is usually available. To overcome this obstacle, this paper presents a proposed semi-supervised learning method that enables the user to exploit unlabeled sequences to enlarge labeled sequence set, leading to improvement of the performance in finding discriminative motifs. The comparative experimental evaluation of the proposed semi-supervised learning shows that it can improve considerably the predictive accuracy of the found motifs.
['Thi Nhan Le', 'Tu Bao Ho']
A semi-supervised method for discriminative motif finding and its application to hepatitis c virus study