title: Efficient prediction methods for selecting effective siRNA sequences.
abstract: 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. This paper first reviewed the recently reported siRNA design guidelines and clarified the problems concerning the guidelines. It then proposed two prediction methods-Radial Basis Function (RBF) network and decision tree learning-and their combined method for selecting effective siRNA target sequences from many possible candidate sequences. They are quite different from the previous score-based siRNA design techniques and can predict the probability that a candidate siRNA sequence will be effective. The methods imply high estimation accuracy for selecting candidate siRNA sequences.
authors.affiliation: Toyo University, Ora-gun Gunma, Japan. s_takasaki@toyonet.toyo.ac.jp
authors.forename: Shigeru
authors.initials: S
authors.lastname: Takasaki
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D017076:Computer-Aided Design; D030561:Databases, Nucleic Acid; D003663:Decision Trees; D020868:Gene Silencing; D006801:Humans; D016571:Neural Networks, Computer; D011336:Probability; D034622:RNA Interference; D034741:RNA, Small Interfering / Q000235:genetics* / Q000378:metabolism; D012372:ROC Curve
chemical_list: D034741:RNA, Small Interfering