A Descriptive Method for Generating siRNA Design Rules
Short-interfering RNAs (siRNAs) suppress gene expression through a process called RNA interference (RNAi). Current research focuses on finding design principles or rules for siRNAs and using them to artificially generate siRNAs with high efficiency of gene knockdown ability. Design rules have been reported by analyzing biology experiments and applying learning methods. However, possible good design rules or hidden characteristics remain undetected. In contribution to computational methods for finding design rules which are mostly employed by discriminative learning techniques, in this paper we propose a novel descriptive method to discover two design rules for effective siRNA sequences with 19 nucleotides (nt) and 21 nt in length that have important characteristics of previous design rules and contain new characteristics of highly effective siRNA. The key idea of the method is first to transform siRNAs to transactions then apply an Apriori adaptation with automatic min_support values to detect descriptive rules for effective and ineffective siRNAs. Rational design rules are created by analyzing graphical representations of descriptive rules. Experimental evaluation on the two siRNA data sets including 5737 siRNA sequences shown that our design rules are promising to design siRNAs effectively.
KeywordsRNAi siRNA Apriori algorithm discriminative learning techniques descriptive method design rules
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