DiscMLA: AUC-based discriminative motif learning

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 large-scale datasets. Unlike previous approaches, DiscMLA tries to optimize AUC directly during motifs searching. In addition, based on an observation, some novel processes are designed for accelerating DiscMLA. The experimental results show that our approach substantially outperforms previous methods on discriminative motif learning problems. DiscMLA´ stability, discrimination and validity will help to exploit high-throughput datasets and answer many fundamental biological questions.