Computing vs. Genetics - Advancing Artificial Intelligence Through Biological Process Applications - page 180

S2. Try to associate the possibilities of “triplets
versus generated amino acids” relationships
within the gene causing the disease and
establish, if possible, a causal relationship
and, if not, a correlation. For this purpose,
we have to define the right sample size.
S3. If a causality or correlation greater than 0.8
is established, go to S1 with a new case.
If the number of cases is greater than the
proposed sample size and causality or cor-
relation was established for all cases, accept
the hypothesis, if not, reject it.

Kemeny, J. G. (1955). Man Viewed as a Machine .
Scientific American, 192 (4), 58-67.

King, J. L. & Jukes, T. H. (1969). Non Darwinian
Evolution. Science , 164, 788-798.

Koza, J.R. (1992). Genetic Programming . Read-
ing, MA: The MIT Press.

Michalewitz, Z . (1992). Genetic Algorithms +
Data Structures = Evolutionary Programs . New
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Pazos, J. (2000). El Criptoanálisis en el Desci-
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la Información (pp. 267-351). Santiago de Com-
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Of course, when genetic code has been com-
pletely deciphered, this type of hypotheses will
make no sense, because the DNA will explain its
message and its resultant consequences. But, in
the meantime, it is a way of understanding why
some diseases occur. This will, of course, lead
to its prevention, cure or, at least, to the relief of
its effects, through genetic engineering.

Penrose, L. J. (1974). Máquinas que se Autor-
reproducen . In R. Canap, et al., Matematicas en
las Ciencias del Comportamiento (pp: 270-289) .
Madrid: Alianza Editorial, S.A.

Quevedo, F. de. (1995). Poesía Completa I (pp:
43-44) . Madrid: Turner Libros, S.A..

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