Selecting effective siRNA sequences by using radial basis function network and decision tree learning.

ABSTRACT:

Background

Although short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells, its gene silencing efficacy varies markedly and there are only a few consistencies among the recently reported design rules/guidelines for selecting siRNA sequences effective for mammalian genes. Another shortcoming of the previously reported methods is that they cannot estimate the probability that a candidate sequence will silence the target gene.

Results

We propose two prediction methods for selecting effective siRNA target sequences from many possible candidate sequences, one based on the supervised learning of a radial basis function (RBF) network and other based on decision tree learning. They are quite different from the previous score-based siRNA des

SUBMITTER: Takasaki S 

PROVIDER: S-EPMC1764479 | biostudies-literature | 2006 Dec

REPOSITORIES: biostudies-literature