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.
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References
Benenson, Y., Gil, B., Ben-Dor, U., Adar, R., Shapiro, E.: An autonomous molecular computer for logical control of gene expression. Nature 429, 423–429 (2004)
Baum, E.B.: Building an associative memory vastly larger than the brain. Science 268, 583–585 (1995)
Chen, J., Deaton, R., Wang, Y.-Z.: A DNA-based memory with in vitro learning and associative recall. In: Chen, J., Reif, J.H. (eds.) DAN 2003. LNCS, vol. 2943, pp. 145–156. Springer, Heidelberg (2004)
Cheok, M.H., et al.: Treatment-specific changes in gene expression discriminate in vivo drug response in human leukemia cells. Nature Genetics 34, 85–90 (2003)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Reif, J.H., LaBean, T.H., Pirrung, M., Rana, V.S., Guo, B., Kingsford, C., Wickham, G.S.: Experimental construction of very large scale DNA databases with associative search capability. In: Jonoska, N., Seeman, N.C. (eds.) DNA 2001. LNCS, vol. 2340, pp. 231–247. Springer, Heidelberg (2002)
Rose, J.A., Deaton, R.J., Hagiya, M., Suyama, A.: A DNA-based in vitro genetic program. Journal of Biological Physics 28, 493–498 (2002)
Sakakibara, Y.: Solving computational learning problems with Boolean formulae on DNA computers. In: DNA6. LNCS, vol. 2052, pp. 220–230. Springer, Heidelberg (2001)
SantaLucia, J., Hicks, D.: The thermodynamics of DNA structural motifs. Annu. Rev. Biophys. Biomol. Struct. 33, 415–440 (2004)
Shackelford, G., Volper, D.: Learning k-DNF with noise in the attributes. In: COLT 1988: Proc. First Annual Workshop on Computational Learning Theory, pp. 97–103 (1988)
Valiant, L.: Robust logics. In: Proc. ACM Symposium on the Theory of Computing (STOC 1999), pp. 642–651 (1999)
Wartel, R.M., Benight, A.S.: Thermal denaturation of DNA molecules: A comparison of theory with experiments. Physics Reports 126(2), 67–107 (1985)
Zhang, B.-T., Jang, H.-Y.: A Bayesian algorithm for in vitro molecular evolution of pattern classifiers. In: Ferretti, C., Mauri, G., Zandron, C. (eds.) DNA 2004. LNCS, vol. 3384, pp. 294–303. Springer, Heidelberg (2005)
Zhang, B.-T., Mühlenbein, H.: Balancing accuracy and parsimony in genetic programming. Evolutionary Computation 3(1), 17–38 (1995)
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Zhang, BT., Jang, HY. (2006). Molecular Learning of wDNF Formulae. In: Carbone, A., Pierce, N.A. (eds) DNA Computing. DNA 2005. Lecture Notes in Computer Science, vol 3892. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11753681_34
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DOI: https://doi.org/10.1007/11753681_34
Publisher Name: Springer, Berlin, Heidelberg
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