Source:
Proceedings of the 1998 IEEE World Congress on Computational Intelligence, IEEE Press, Anchorage, Alaska, USA, p.212--217 (1998)
URL:
http://www.genetic-programming.com/jkpdf/icec1998.pdf
Keywords:
genetic algorithms;
genetic programming
Abstract:
As newly sequenced proteins are deposited into the
world' s ever-growing archive of protein sequences,
they are typically immediately tested by various
computerized algorithms for clues as to their
biological structure and function. One question about a
new protein involves its cellular location - that is,
where the protein resides in a living organism
(extracellular, intracellular, etc.). A 1997 paper
reported a human-created five-way algorithm for
cellular location created using statistical techniques
with 76% accuracy.
This paper describes a two-way classification algorithm
that was evolved using genetic programming with 83%
accuracy for determining whether a protein is
extracellular. Unlike the statistical calculation, the
genetically evolved algorithm employs a large and
varied arsenal of computational capabilities, including
arithmetic functions, conditional operations,
subroutines, iterations, memory, data structures,
set-creating operations, macro definitions, recursion,
etc. The genetically evolved classification algorithm
can be viewed as an extension (which we call a
programmatic motif) of the conventional notion of a
protein motif. The genetically evolved program
constitutes an instance of an evolutionary computation
technique producing a solution to a problem that is
competitive with that produced using human
intelligence.