@inproceedings{koza:1994:cpstd,
	title = {Evolution of a computer program for classifying
                 protein segments as transmembrane domains using genetic
                 programming},
	author = {John R. Koza},
	booktitle = {Proceedings of the Second International Conference on
                 Intelligent Systems for Molecular Biology},
	editor = {Russ Altman and Douglas Brutlag and Peter Karp and Richard Lathrop and David Searls},
	pages = {244--252},
	publisher = {AAAI Press},
	url = {http://www.genetic-programming.com/jkpdf/ismb1994.pdf},
	year = {1994},
	biburl = {http://www.bibsonomy.org/bibtex/2e8e8f5a5cf683017b2efa746562c5326/brazovayeye},
	abstract = {The recently-developed genetic programming paradigm is
                 used to evolve a computer program to classify a given
                 protein segment as being a transmembrane domain or
                 non-transmembrane area of the protein. Genetic
                 programming starts with a primordial ooze of randomly
                 generated computer programs composed of available
                 programmatic ingredients and then genetically breeds
                 the population of programs using the Darwinian
                 principle of survival of the fittest and an analog of
                 the naturally occurring genetic operation of crossover
                 (sexual recombination). Automatic function definition
                 enables genetic programming to dynamically create
                 subroutines dynamically during the run. Genetic
                 programming is given a training set of
                 differently-sized protein segments and their correct
                 classification (but no biochemical knowledge, such as
                 hydrophobicity values). Correlation is used as the
                 fitness measure to drive the evolutionary process. The
                 best genetically-evolved program achieves an
                 out-of-sample correlation of 0.968 and an out-of-sample
                 error rate of 1.6percent. This error rate is better
                 than that reported for four other algorithms reported
                 at the First International Conference on Intelligent
                 Systems for Molecular Biology. Our genetically evolved
                 program is an instance of an algorithm discovered by an
                 automated learning paradigm that is superior to that
                 written by human investigators.},
	publisher_address = {Menlo Park, CA, USA},
	keywords = {algorithms, genetic programming }
}

@book{searls1992,
	title = {Potash},
	address = {Gordon Press Publishers]},
	annote = {16 Bl},
	author = {{James P.} Searls},
	howpublished = {[New York, NY},
	url = {http://gso.gbv.de/DB=2.1/CMD?ACT=SRCHA&SRT=YOP&IKT=1016&TRM=ppn+229251994&sourceid=fbw_bibsonomy},
	year = {1992},
	biburl = {http://www.bibsonomy.org/bibtex/26b0807eeb3e6aa852da349c99edfc5a6/fbw},
	description = {imported},
	isbn = {0-8490-8993-X},
	keywords = {Kaliindustrie }
}

@article{Lilien.2002,
	title = {Performance Assessment of the Lead User Idea-Generation Process for New Product Development},
	author = {Gary L. Lilien and Pamela D. Morrison and Kathleen Searls and Mary Sonnack and Eric Von Hippel},
	journal = {Management Science},
	number = {8},
	pages = {1042-1059},
	volume = {48},
	year = {2002},
	biburl = {http://www.bibsonomy.org/bibtex/29c47b6462b7341bd3ace99d86922c886/callagialla},
	abstract = {Traditional idea generation techniques based on customer input usually collect information on new product needs from a random or typical set of customers. The "lead user process" takes a different approach. It collects information about both needs and solutions from users at the leading edges of the target market, as well as from users in other markets that face similar problems in a more extreme form. This paper reports on a natural experiment conducted within the 3M Company on the effect of the lead user (LU) idea-generation process relative to more traditional methods. 3M is known for its innovation capabilities-- and we find that the LU process appears to improve upon those capabilities. Annual sales of LU product ideas generated by the average LU project at 3M are conservatively projected to be $146 million after five years--more than eight times higher than forecast sales for the average contemporaneously conducted "traditional" project. Each funded LU project is projected to create a new major product line for a 3M division. As a direct result, divisions funding LU project ideas are projecting their highest rate of major product line generation in the past 50 years.},
	issn = {0025-1909},
	keywords = {diss }
}

@article{searls:annrevgenomicshumgenet00,
	title = {Bioinformatics Tools for Whole Genomes},
	annote = {risto: aging pathway project},
	author = {David B. Searls},
	journal = {Annual Reviews of Genomics and Human Genetics},
	pages = {251--279},
	volume = {1},
	year = {2000},
	biburl = {http://www.bibsonomy.org/bibtex/2c704659dfddf0e121ec4a53b46584166/idsia},
	priority = {2}, citeulike-article-id = {2379028},
	keywords = {nn }
}

@article{searls:annrevgenomicshumgenet00,
	title = {Bioinformatics Tools for Whole Genomes},
	annote = {risto: aging pathway project},
	author = {David B. Searls},
	journal = {Annual Reviews of Genomics and Human Genetics},
	pages = {251--279},
	volume = {1},
	year = {2000},
	biburl = {http://www.bibsonomy.org/bibtex/2c704659dfddf0e121ec4a53b46584166/schaul},
	description = {idsia},
	priority = {2}, citeulike-article-id = {2379028},
	keywords = {nn }
}

@article{Lutz2005,
	title = {Managing genomic and proteomic knowledge},
	author = {Michael W. Lutz and Patrick V. Warren and Rob W. Gill and David B. Searls},
	journal = {Drug Discovery Today: Technologies},
	number = {3},
	pages = {197-204},
	url = {http://www.sciencedirect.com/science/article/B75D6-4GYH7VV-1/2/18aacd74d0c785fbf90f1cd3d38ed004},
	volume = {2},
	year = {2005},
	biburl = {http://www.bibsonomy.org/bibtex/24f512617c29e8d91a75bbdb15e46b45c/p_ansell},
	description = {Bioinformatics Workflow Systems},
	owner = {peter}, timestamp = {2007.04.18 11:10},
	keywords = {imported }
}

@article{searls:02a,
	title = {The language of genes},
	author = {D. B. Searls},
	journal = {Nature},
	number = {420},
	pages = {211--217},
	year = {2002},
	biburl = {http://www.bibsonomy.org/bibtex/290dd88b2178a7448156c12575ebbe7ee/hanappe},
	keywords = {imported }
}

@inproceedings{conf/gcb/Searls97,
	title = {Languages, Automata, and Macromolecules.},
	author = {David B. Searls},
	booktitle = {German Conference on Bioinformatics},
	pages = {19},
	url = {http://dblp.uni-trier.de/db/conf/gcb/gcb1997.html#Searls97},
	year = {1997},
	biburl = {http://www.bibsonomy.org/bibtex/27b11a0b767f6b34cfe450fd22e48d4b0/dblp},
	description = {dblp},
	date = {2005-02-04},
	keywords = {dblp }
}

@inproceedings{conf/ismb/ManochaZ94,
	title = {Kinematic Manipulation of Molecular Chains Subject to Rigid Constraint.},
	author = {Dinesh Manocha and Yunshan Zhu},
	booktitle = {ISMB},
	crossref = {conf/ismb/1994},
	editor = {Russ B. Altman and Douglas L. Brutlag and Peter D. Karp and Richard H. Lathrop and David B. Searls},
	pages = {285-293},
	publisher = {AAAI},
	url = {http://dblp.uni-trier.de/db/conf/ismb/ismb1994.html#ManochaZ94},
	year = {1994},
	biburl = {http://www.bibsonomy.org/bibtex/2ec8e06815aa29c36316a008050f980ff/dblp},
	description = {dblp},
	isbn = {0-929280-68-7}, date = {2004-12-21},
	keywords = {dblp }
}

@inproceedings{conf/ismb/GaasterlandMLC94,
	title = {Assigning Function to CDS Through Qualified Query Answering: Beyond Alignment and Motifs.},
	author = {Terry Gaasterland and Natalia Maltsev and Jorge Lobo and Guo-Hua Chen},
	booktitle = {ISMB},
	crossref = {conf/ismb/1994},
	editor = {Russ B. Altman and Douglas L. Brutlag and Peter D. Karp and Richard H. Lathrop and David B. Searls},
	pages = {130-137},
	publisher = {AAAI},
	url = {http://dblp.uni-trier.de/db/conf/ismb/ismb1994.html#GaasterlandMLC94},
	year = {1994},
	biburl = {http://www.bibsonomy.org/bibtex/2d145438600b472e5620e622079e4164f/dblp},
	description = {dblp},
	isbn = {0-929280-68-7}, date = {2004-11-04},
	keywords = {dblp }
}