Selecting highly effective siRNAs by their modified entropies with mini-clusters

Synthetic sources of double-stranded RNA can enter the RNAi pathway at various points. The most basic approach involves transfection of effective siRNA duplexes that resemble Dicer products. Synthetic sources of double-stranded RNA can enter the RNAi pathway at various points. The most basic approach involves transfection of siRNA duplexes that resemble Dicer products. The approaches of designing effective siRNAs are classified into to two groups: the score-based algorithms and the machine learning classification algorithms. The first group approaches, which focus on finding the common features of effective siRNAs, though they initially and intuitively provide guidelines for siRNAs design, are far from satisfied due to low sensitivity and specificity. The other approaches are motivated by statistical learning theory, attempt to classify the siRNA into effective or ineffective class. Although those two-class classifiers provide a promising way to screen potentially effective siRNAs, it is difficult to decide the boundary between the two classes.A novel method of distinguishing effective siRNAs is reported that combines the advantages of the score-based algorithms and the machine learning classification algorithms, where it use the modified entropies of siRNAs as feature indicator of siRNAs and split siRNAs into many smaller effective or ineffective subclasses by a mini-clusters algorithm. When the modified entropies with mini-clusters algorithm apply to experimental siRNAs data, all effective siRNAs can be identified correctly, and no more than 17% ineffective siRNAs are misidentified as effective ones.