TY - GEN
T1 - Promoter prediction based on a multiple instance learning scheme
AU - Zhou, Xuefeng
AU - Ruan, Jianhua
AU - Zhang, Weixiong
PY - 2010
Y1 - 2010
N2 - Core promoters are crucial regions for initiation of gene transcription. Identification of core promoters is important to the understanding of transcriptional regulation and elucidation of relationships among genes of an organism. Experimentally locating core promoters is laborious and costly. Therefore, it is desirable to develop computational approaches to support and complement experimental methods. However, computational prediction of core promoters of eukaryotic species is challenging. In this paper, we first formulate the core promoter prediction problem as a variation of the multiple instance learning problem. We then develop a new algorithm for identifying core promoters with a high positive prediction rate and a high sensitivity. Since many computational biology problems can be formulated under the multiple instance learning paradigm, our approach may inspire future research of applying multiple instance learning techniques to complex biology problems and our method may have broad potential applications.
AB - Core promoters are crucial regions for initiation of gene transcription. Identification of core promoters is important to the understanding of transcriptional regulation and elucidation of relationships among genes of an organism. Experimentally locating core promoters is laborious and costly. Therefore, it is desirable to develop computational approaches to support and complement experimental methods. However, computational prediction of core promoters of eukaryotic species is challenging. In this paper, we first formulate the core promoter prediction problem as a variation of the multiple instance learning problem. We then develop a new algorithm for identifying core promoters with a high positive prediction rate and a high sensitivity. Since many computational biology problems can be formulated under the multiple instance learning paradigm, our approach may inspire future research of applying multiple instance learning techniques to complex biology problems and our method may have broad potential applications.
UR - http://www.scopus.com/inward/record.url?scp=77958062831&partnerID=8YFLogxK
U2 - 10.1145/1854776.1854817
DO - 10.1145/1854776.1854817
M3 - Conference contribution
AN - SCOPUS:77958062831
SN - 9781450304382
T3 - 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010
SP - 295
EP - 301
BT - 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010
T2 - 2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010
Y2 - 2 August 2010 through 4 August 2010
ER -