- author = "John R. Koza and Forrest H {Bennett III} and David Andre",
- title = "Classifying Proteins as Extracellular using Programmatic Motifs and Genetic Programming",
- booktitle = "Proceedings of the 1998 IEEE World Congress on Computational Intelligence",
- year = "1998",
- pages = "212--217",
- address = "Anchorage, Alaska, USA",
- month = "5-9 " # may,
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, arithmetic functions, biological structure, cellular location, computerised algorithms, conditional operations, data structures, evolutionary computation technique, extracellular proteins, genetically evolved algorithm, genetically evolved classification algorithm, human created five way algorithm, human intelligence, living organism, macro definitions, newly sequenced proteins, programmatic motif, programmatic motifs, protein classification, protein motif, protein sequences, set creating operations, statistical techniques, subroutines, two way classification algorithm, biology computing, molecular biophysics, pattern classification, proteins",
- ISBN = "0-7803-4869-9",
-
URL = "
http://www.genetic-programming.com/jkpdf/icec1998.pdf",
-
DOI = "
doi:10.1109/ICEC.1998.699503",
- file = "c037.pdf",
- size = "6 pages",
-
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 76percent accuracy.
This paper describes a two-way classification algorithm that was evolved using genetic programming with 83percent 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.",
- notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence",
