On September 30, 2021, Xue, Tian; Zhang, Shengli; Qiao, Huijuan published an article.Recommanded Product: 443-72-1 The title of the article was i6mA-VC: A Multi-Classifier Voting Method for the Computational Identification of DNA N6-methyladenine Sites. And the article contained the following:
Abstract: DNA N6-methyladenine (6 mA), as an essential component of epigenetic modification, cannot be neglected in genetic regulation mechanism. Most of the established machine learning methods have a single dataset. Although some of them have achieved cross-species prediction, their results are not satisfactory. Therefore, we designed a novel statistical model called i6mA-VC to improve the accuracy for 6 mA sites. On the one hand, kmer and binary encoding are applied to extract features, and then gradient boosting decision tree (GBDT) embedded method is applied as the feature selection strategy. After fusing the two optimal features, a voting classifier based on gradient boosting decision tree (GBDT), light gradient boosting machine (LightGBM) and multilayer perceptron classifier (MLPC) is constructed for final classification and prediction. The accuracy of Rice dataset and M.musculus dataset with five-fold cross-validation are 0.888 and 0.967, resp. The cross-species dataset is selected as independent testing dataset, and the accuracy reaches 0.848. Through rigorous experiments, it is demonstrated that the proposed predictor is convincing and applicable. The development of i6mA-VC predictor will become an effective way for the recognition of N6-methyladenine sites, and it will also be beneficial for biol. geneticists to further study gene expression and DNA modification. The experimental process involved the reaction of N-Methyl-7H-purin-6-amine(cas: 443-72-1).Recommanded Product: 443-72-1
The Article related to n6methyladenine machine learning dna sequence genetics, dna n6-methyladenine sites, light gradient boosting machine, multilayer perceptron classifier, ring-function-hydrogen-chemical properties, voting and other aspects.Recommanded Product: 443-72-1
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