Alternative splice site recognition based on a new fuzzy support vector machine.

@article{Li2013AlternativeSS,
  title={Alternative splice site recognition based on a new fuzzy support vector machine.},
  author={Xiao-xia Li and Bo Sun and Cheng Li},
  journal={Acta biochimica et biophysica Sinica},
  year={2013},
  volume={45 5},
  pages={
          425-7
        }
}
Accurate alternative splice site (ASS) recognition is an important and difficult topic in the gene identification, and the average recognition rate is still ,85% [1]. Many statistical pattern recognition methods, such as neural networks (NNs) and support vector machine (SVM), were used for this task [2,3]. Among them, SVM can construct a good highdimensional learning model in the case of limited training set size and has good generalization ability, which exhibits many unique advantages in… 
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