- author = "John Koza and Forrest Bennett and David Andre",
- title = "Using Programmatic Motifs and Genetic Programming to Classify Protein Sequences as to Extracellular and Membrane Cellular Location",
- booktitle = "Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming",
- year = "1998",
- editor = "V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben",
- volume = "1447",
- series = "LNCS",
- address = "Mission Valley Marriott, San Diego, California, USA",
- publisher_address = "Berlin",
- month = "25-27 " # mar,
- publisher = "Springer-Verlag",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "3-540-64891-7",
-
URL = "
http://www.genetic-programming.com/jkpdf/ep1998.pdf",
-
DOI = "
doi:10.1007/BFb0040753",
- abstract = "As newly sequenced proteins are deposited into the world's ever-growing archive of protein sequences, they are typically immediately tested by various algorithms for clues as to their biological structure and function. One question about a new protein involves its cellular location p; that is, where the protein resides in a living organism (extracellular, membrane, etc.). A human-created five-way algorithm for cellular location using statistical techniques with 76% accuracy was recently reported. This paper describes a two-way algorithm that was evolved using genetic programming with 83% accuracy for determining whether a protein is extracellular and with 89% accuracy for membrane proteins. 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.",
-
notes = "EP-98.
",
