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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ping Xuan Mao-zu Guo Lei-lei Shi Jun Wang Xiao-yan Liu Wen-bin Li Ying-peng Han |
| Copyright Year | 2010 |
| Description | Author affiliation: Soybean Research Institute, Northeast Agricultural University, Harbin Heilongjiang 150030 P.R. China (Wen-bin Li; Ying-peng Han) || The Computing Laboratory, University of Kent, Canterbury, CT2 7NF, U.K. (Lei-lei Shi) || School of Computer Science and Technology, Harbin Institute of Technology, Harbin Heilongjiang 150001, P.R. China (Ping Xuan; Mao-zu Guo; Jun Wang; Xiao-yan Liu) |
| Abstract | To solve the class imbalance problem in classification of pre-miRNAs with ab initio method, a novel sample selection method is proposed according to the characteristics of pre-miRNAs. Real/pseudo pre-miRNAs are clustered based on their stem similarity and their distribution in high dimensional sample space respectively. The training samples are selected according to the sample density of each cluster. Experimental results are validated by the cross validation and other testing datasets composed of human real/pseudo pre-miRNAs. When compared with the previous study, microPred, our classifier miRNAPred is nearly 12% greater in total accuracy. Our sample selection algorithm is useful to construct more efficient classifier for classification of real pre-miRNAs and pseudo hairpin sequences. |
| Starting Page | 549 |
| Ending Page | 552 |
| File Size | 359124 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424483068 |
| e-ISBN | 9781424483075 |
| DOI | 10.1109/BIBM.2010.5706626 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Support vector machines Humans Clustering algorithms Feature extraction Bioinformatics Testing |
| Content Type | Text |
| Resource Type | Article |
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