Imbalance learning for the prediction of N <sup>6</sup> -Methylation sites in mRNAs
- Publication Type:
- Journal Article
- BMC Genomics, 2018, 19 (1)
- Issue Date:
© 2018 The Author(s). Background: N 6 -methyladenosine (m 6 A) is an important epigenetic modification which plays various roles in mRNA metabolism and embryogenesis directly related to human diseases. To identify m 6 A in a large scale, machine learning methods have been developed to make predictions on m 6 A sites. However, there are two main drawbacks of these methods. The first is the inadequate learning of the imbalanced m 6 A samples which are much less than the non-m 6 A samples, by their balanced learning approaches. Second, the features used by these methods are not outstanding to represent m 6 A sequence characteristics. Results: We propose to use cost-sensitive learning ideas to resolve the imbalance data issues in the human mRNA m 6 A prediction problem. This cost-sensitive approach applies to the entire imbalanced dataset, without random equal-size selection of negative samples, for an adequate learning. Along with site location and entropy features, top-ranked positions with the highest single nucleotide polymorphism specificity in the window sequences are taken as new features in our imbalance learning. On an independent dataset, our overall prediction performance is much superior to the existing predictors. Our method shows stronger robustness against the imbalance changes in the tests on 9 datasets whose imbalance ratios range from 1:1 to 9:1. Our method also outperforms the existing predictors on 1226 individual transcripts. It is found that the new types of features are indeed of high significance in the m 6 A prediction. The case studies on gene c-Jun and CBFB demonstrate the detailed prediction capacity to improve the prediction performance. Conclusion: The proposed cost-sensitive model and the new features are useful in human mRNA m 6 A prediction. Our method achieves better correctness and robustness than the existing predictors in independent test and case studies. The results suggest that imbalance learning is promising to improve the performance of m 6 A prediction.
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