A Computational Model Based on Intelligence Computation for Classifying Enhancers

Qingling Yan, Xuan Xiao, Wangren Qiu


As a crucial DNA element, enhancer usually can be classified as strong enhancer or weak enhancer. To make some improvement for identifying enhancers and their subsets, the paper constructed an intelligent computational model by extracting features from bi-profile Bayes and pseudo amino acid components. Moreover, the combination of features, jackknife cross validation and Support Vector Machine were tested on the rigorous dataset. It has been achieved that accuracyof 77.30% on identifying enhancers and non-enhancers, and accuracy of 68.22% on classifying strong enhancers and weak enhancers. Eventually, the performance we achieved is satisfactory.


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