Recognition of the splice sites based on improved self-organizing feature maps

A clustering method for large quantities of high-dimensional data which combining unscented Kalman filter(UKF) with self-organizing feature maps(SOFM) was proposed to improve the recognition accuracy of splice sites among the gene sequences.The mean and variance of width of the neighborhood function were parameterized by unscented transform(UT) and then predicted by UKF to complete adaptive process of SOFM parameters.Tests on recognizing gene splice sites show that the proposed method has higher recognition accuracy comparing with SOFM and EFK-based parameter self-adaptive methods,which verifies its validity and feasibility.