The Community for Technology Leaders
2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2015)
Washington, DC, USA
Nov. 9, 2015 to Nov. 12, 2015
ISBN: 978-1-4673-6798-1
pp: 250-255
Hongbo Zhang , College of Electronics and Information Engineering, Tongji University, Shanghai, China
Lin Zhu , College of Electronics and Information Engineering, Tongji University, Shanghai, China
Deshuang Huang , College of Electronics and Information Engineering, Tongji University, Shanghai, China
ABSTRACT
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.
INDEX TERMS
ChIP-seq, Discriminative motif learning, AUC
CITATION

Hongbo Zhang, Lin Zhu and Deshuang Huang, "DiscMLA: AUC-based discriminative motif learning," 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Washington, DC, USA, 2015, pp. 250-255.
doi:10.1109/BIBM.2015.7359688
184 ms
(Ver 3.3 (11022016))