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The rapid development of high-throughput sequencing technology provides unique opportunities for studies of transcription factor binding, while also bringing new computational challenges. Recently, a series of discriminative motif discovery (DMD) methods have been proposed and offer promising solutions for addressing these challenges. However, because of the huge computational cost, most of them have...
The recently proposed family of discriminative motif finders is promising for harnessing the power of large quantities of accumulated high-throughput experimental data, however, they have to sacrifice accuracy by employing simplified statistical models during the learning process. In this paper, we propose a new approach called Discriminative Motif Learning via AUC (DiscMLA) to discover motifs on...
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