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Short interfering RNAs
(siRNAs) can be used to suppress
gene expression and have many potential applications
in therapy, yet how to design an effective siRNA is still not
clear.
Based on the MPI basic principles, a number of
siRNA design tools have been developed in the past two
years.
The set of candidates output by these tools is
usually large and often contains some ineffective siRNAs.
In view of this, we initiate the study of filtering ineffective
siRNAs.
The contribution of this paper is two-fold.
Firstly, we propose a fair scheme to compare existing
design tools based on real data
in the literature.
Secondly, we attempt to improve the MPI principles and existing tools
by an algorithm that can filter ineffective siRNAs.
The algorithm is based on some new observations on the
secondary structure, which we have verified by
AI techniques (decision trees and support vector machines).
We have tested our algorithm together with the MPI principles
and the existing tools.
The results show that
our filtering algorithm is effective.
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