TY - GEN
T1 - A novel genome-wide polyadenylation sites recognition system based on condition random field
AU - Han, Jiuqiang
AU - Zhang, Shanxin
AU - Liu, Jun
AU - Liu, Ruiling
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/2
Y1 - 2014/11/2
N2 - Polyadenylation including the cleavage of pre-mRNA and addition of a stretch of adenosines to the 3'-end is an essential step of pre-mRNA processing in eukayotes. The known regulatory role of polyadenylation in mRNA localization, stability, and translation and the emerging link between poly(A) and disease states underline the necessary to fully characterize polyadenylation sites. Several artificial intelligence methods have been proposed for poly(A) sites recognition. However, these methods are suitable to small subsets of genome sequences. It is necessary to propose a method for genome-wide recognition of poly(A) sites. Recent efforts have found a lot of poly(A) related factors on DNA level. Here, we proposed a novel genome-wide poly(A) recognition method based on the Condition Random Field (CRF) by integrating multiple features. Compared with the polya-svm (the most accurate program for prediction of poly(A) sites till date), our method had a higher performance with the area under ROC curve(0.8621 versus 0.6796). The result suggests that our method is an effective method in genome wide poly(A) sites recognition.
AB - Polyadenylation including the cleavage of pre-mRNA and addition of a stretch of adenosines to the 3'-end is an essential step of pre-mRNA processing in eukayotes. The known regulatory role of polyadenylation in mRNA localization, stability, and translation and the emerging link between poly(A) and disease states underline the necessary to fully characterize polyadenylation sites. Several artificial intelligence methods have been proposed for poly(A) sites recognition. However, these methods are suitable to small subsets of genome sequences. It is necessary to propose a method for genome-wide recognition of poly(A) sites. Recent efforts have found a lot of poly(A) related factors on DNA level. Here, we proposed a novel genome-wide poly(A) recognition method based on the Condition Random Field (CRF) by integrating multiple features. Compared with the polya-svm (the most accurate program for prediction of poly(A) sites till date), our method had a higher performance with the area under ROC curve(0.8621 versus 0.6796). The result suggests that our method is an effective method in genome wide poly(A) sites recognition.
UR - https://www.scopus.com/pages/publications/84929484966
U2 - 10.1109/EMBC.2014.6944687
DO - 10.1109/EMBC.2014.6944687
M3 - 会议稿件
C2 - 25571055
AN - SCOPUS:84929484966
T3 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
SP - 4755
EP - 4758
BT - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Y2 - 26 August 2014 through 30 August 2014
ER -