Abstract
This project presents an updated method for classification of polymerase chain reaction primers in mice using finite state classifiers. This is done to compensate for many lab, organism and chemical specific factors that are costly. using Finite State Classifiers can help decrease the number of primers that fail to amplify correctly. for training these classifiers, five different evolutionary algorithms that use an incremental fitness reward are used. Variations to the number of generations and the values in the fitness reward are examined, and the resulting designs are presented. by controlling the fitness reward correctly, there is a potential to develop classifiers with a high likelihood of accepting only good primers. the proposed tool can act as a post-production add-on to the standard primer picking algorithm for gene expression detection in mice to compensate for local factors that may induce errors. © IEEE.
Recommended Citation
S. R. Yadav and S. M. Corns, "Improved PCR Design for Mouse DNA by Training Finite State Machines," 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010, pp. 249 - 253, article no. 5510701, Institute of Electrical and Electronics Engineers, Aug 2010.
The definitive version is available at https://doi.org/10.1109/CIBCB.2010.5510701
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-142446766-2
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
20 Aug 2010
