A computational method for small molecule-RNA binding sites identification by utilizing position specificity and...

Some computational methods have been given for small molecule-RNA binding site identification due to that it plays a significant role in revealing biology function researches. However, it is still challenging to design an accurate model, especially for MCC. We designed a feature extraction technology from two aspects (position specificity and complex network information). Specifically, complex network was employed to express the space topological structure and sequence position information for improving prediction effect. Then, the features fused position specificity and complex network information were input into random forest classifier for model construction. The AUC of 88.22%, 77.92% and 81.46% were obtained on three independent datasets (RB19, CS71, RB78). Compared with the existing method, the best MCC were obtained on three datasets, which were 8.19%, 0.59% and 4.35% higher than the state-of-the-art prediction methods, respectively. The outstanding performances show that our method is a powerful tool to identify RNA binding sites, helping to the design RNA-targeting small molecule drugs. The data and resource codes are available at https://github.com/Kangxiaoneuq/PCN_RNAsite.