Various computational methods have been proposed to predict pre-miRNAs. Most of these methods employed the machine learning techniques to build their prediction models, which treated this problem as a binary classification task to discriminate the real pre-miRNAs from false pre-miRNAs. These methods are different in the feature selections and machine learning algorithms or operation engines. The machine learning algorithms usually used in this field include Support Vector Machine, Random Forest, Hidden Markov Model, Covariant Discrimination or Naive Bayes, and Linear Genetic Programming. The secondary structure is an important feature used in the computational methods, because most of the pre-miRNAs have the characteristic of stem-loop hairpin structures. Mir-abela is an SVM-based method trained with 16 statistic features computed from the entire hairpin structure. Triplet-SVM employed a SVM classifier to train 32 local triplet sequence-structure features. Later, MiPred improved Triplet-SVM by employing the Random Forest classifier trained with the local triplet sequence-structure features, minimum of free energy, and P-values. MiRFinder is a high-throughput pre-miRNA prediction method, which consists of two steps: a LY2109761 search for hairpin candidates and exclusion of the nonrobust structures based on the analysis of 18 parameters by the SVM. All these computational methods could yield quite encouraging results, and each of them did play a role in simulating the development of pre-miRNA identification. However, further work is needed due to the following reasons: The datasets constructed in those methods were too small to reflect the statistical profile of human pre-miRNAs. Most of these methods were trained and tested with a dataset containing only several Y-27632 hundreds of human pre-miRNA samples or pseudo pre-miRNA samples. No cutoff threshold was imposed to rigorously exclude the redundant samples or those with high sequence similarity with others in a same benchmark dataset. Most of these methods only consider the local structure or sequence order information of RNA sequences, and all the global or long range structure or sequence order effects were ignored. In this study, we attempted to improve the accuracy for human pre-miRNA identification from the above three aspects; especially, we focused on how to incorporate the global structure- order effects into the predictor. However, it is difficult to incorporate this kind of information into a statistical predictor because the RNA sequences have different lengths with extremely large number of possible structure patterns.