Molecular Learning of wDNF Formulae

  • Byoung-Tak Zhang
  • Ha-Young Jang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3892)


We introduce a class of generalized DNF formulae called wDNF or weighted disjunctive normal form, and present a molecular algorithm that learns a wDNF formula from training examples. Realized in DNA molecules, the wDNF machines have a natural probabilistic semantics, allowing for their application beyond the pure Boolean logical structure of the standard DNF to real-life problems with uncertainty. The potential of the molecular wDNF machines is evaluated on real-life genomics data in simulation. Our empirical results suggest the possibility of building error-resilient molecular computers that are able to learn from data, potentially from wet DNA data.


Genetic Programming Disjunctive Normal Form Hybridization Reaction Query Pattern Diagnosis Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Byoung-Tak Zhang
    • 1
  • Ha-Young Jang
    • 1
  1. 1.Biointelligence LaboratorySeoul National UniversitySeoulKorea

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