Abstract
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. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Zhang, B. T., & Jang, H. Y. (2006). Molecular learning of wDNF formulae. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3892 LNCS, pp. 427–437). Springer Verlag. https://doi.org/10.1007/11753681_34
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