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Effect of e-MOEA in NACST/Seq

This seminar presents the use of an enhanced multi-objective evolutionary algorithm (e-MOEA) for optimizing probe design targeting human papillomavirus (HPV) genes. The study focuses on 19 genes with predefined candidate regions, addressing objectives such as hairpin stability, melting temperature variation, similarity, and the H-measure across probes while ensuring non-target sequences are excluded. Key improvements include adaptive archiving, density measures, and performance metrics. The findings reveal significant efficiency gains compared to traditional NSGA-II methods, completing the design process in just one day.

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Effect of e-MOEA in NACST/Seq

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  1. Effect of e-MOEA in NACST/Seq 2004. 2. 17. MEC Seminar In-Hee Lee

  2. Probe Design • Working with Bio-Med lab. • Probe design for HPV. • 19 genes. • Pre-defined candidate region. • Objectives: hairpin, tm variation, similarity, h-measure (between probes, non-target) • Constraint: probe sequence must not occur in non-target gene.

  3. With NSGA-II • Popsize 2000, generation 200 • Took 1 week. • Cross-hybridization: at least 11 cases. • Hyther 사용 • Objective values • Haripin: 3038 • H-measure: 35672 • Similarity: 2469 • Tm variation: 2.09008

  4. With e-MOEA • Popsize 2000, gen: 400000 • Took 1 day.

  5. Further Improvements • Density measure • Epsilon: must find appropriate value by trial and error. • Or adaptive archiving? • Real Pareto-front may not be uniform over objective space.

  6. Further Improvements. • Archive size • Fixed or Infinite. • If fixed, which truncation method? • Dominated solutions. • Random victim – worst choice! • Clustering. • Density based.

  7. Further Improvements. • Performance measure • Compatible and complete unary measure does not exist theoretically. • Practically useful unary measure • Convergence: hypervolume • Diversity: crowding distance • Or problem specific performance measure by simulation….

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