Abstract:
Bioinformatics applies computers to problems in molecular biology. Previous
research has not addressed edit metric decoders. Decoders for quaternary
edit metric codes are finding use in bioinformatics problems with
applications to DNA. By using side effect machines we hope to be able to
provide efficient decoding algorithms for this open problem. Two ideas for
decoding algorithms are presented and examined. Both decoders use Side
Effect Machines(SEMs) which are generalizations of finite state automata.
Single Classifier Machines(SCMs) use a single side effect machine to classify
all words within a code. Locking Side Effect Machines(LSEMs) use multiple
side effect machines to create a tree structure of subclassification. The goal
is to examine these techniques and provide new decoders for existing codes.
Presented are ideas for best practices for the creation of these two types of
new edit metric decoders.
