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  • 1
    Electronic Resource
    Electronic Resource
    A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features (1993)
    Springer
    Machine learning 10 (1993), S. 57-78 
    Details
    ISSN: 0885-6125
    Keywords: Nearest neighbor ; exemplar-based learning ; protein structure ; text pronunciation ; instance-based learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of the feature space is required. We introduce a nearest neighbor algorithm for learning in domains with symbolic features. Our algorithm calculates distance tables that allow it to produce real-valued distances between instances, and attaches weights to the instances to further modify the structure of feature space. We show that this technique produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text. Direct experimental comparisons with the other learning algorithms show that our nearest neighbor algorithm is comparable or superior in all three domains. In addition, our algorithm has advantages in training speed, simplicity, and perspicuity. We conclude that experimental evidence favors the use and continued development of nearest neighbor algorithms for domains such as the ones studied here.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022664626993
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    A weighted nearest neighbor algorithm for learning with symbolic features (1993)
    Springer
    Machine learning 10 (1993), S. 57-78 
    Details
    ISSN: 0885-6125
    Keywords: Nearest neighbor ; exemplar-based learning ; protein structure ; text pronunciation ; instance-based learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of the feature space is required. We introduce a nearest neighbor algorithm for learning in domains with symbolic features. Our algorithm calculates distance tables that allow it to produce real-valued distances between instances, and attaches weights to the instances to further modify the structure of feature space. We show that this technique produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text. Direct experimental comparisons with the other learning algorithms show that our nearest neighbor algorithm is comparable or superior in all three domains. In addition, our algorithm has advantages in training speed, simplicity, and perspicuity. We conclude that experimental evidence favors the use and continued development of nearest neighbor algorithms for domains such as the ones studied here.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00993481
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    Paper (German National Licenses)
    Fulltext
  • 3
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    Knockout rats via embryo microinjection of zinc-finger nucleases (2009)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2009-07-25
    Description: The toolbox of rat genetics currently lacks the ability to introduce site-directed, heritable mutations into the genome to create knockout animals. By using engineered zinc-finger nucleases (ZFNs) designed to target an integrated reporter and two endogenous rat genes, Immunoglobulin M (IgM) and Rab38, we demonstrate that a single injection of DNA or messenger RNA encoding ZFNs into the one-cell rat embryo leads to a high frequency of animals carrying 25 to 100% disruption at the target locus. These mutations are faithfully and efficiently transmitted through the germline. Our data demonstrate the feasibility of targeted gene disruption in multiple rat strains within 4 months time, paving the way to a humanized monoclonal antibody platform and additional human disease models.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2831805/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2831805/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Geurts, Aron M -- Cost, Gregory J -- Freyvert, Yevgeniy -- Zeitler, Bryan -- Miller, Jeffrey C -- Choi, Vivian M -- Jenkins, Shirin S -- Wood, Adam -- Cui, Xiaoxia -- Meng, Xiangdong -- Vincent, Anna -- Lam, Stephen -- Michalkiewicz, Mieczyslaw -- Schilling, Rebecca -- Foeckler, Jamie -- Kalloway, Shawn -- Weiler, Hartmut -- Menoret, Severine -- Anegon, Ignacio -- Davis, Gregory D -- Zhang, Lei -- Rebar, Edward J -- Gregory, Philip D -- Urnov, Fyodor D -- Jacob, Howard J -- Buelow, Roland -- 5P01HL082798-03/HL/NHLBI NIH HHS/ -- 5U01HL066579-08/HL/NHLBI NIH HHS/ -- P01 HL082798/HL/NHLBI NIH HHS/ -- P01 HL082798-03/HL/NHLBI NIH HHS/ -- U01 HL066579/HL/NHLBI NIH HHS/ -- U01 HL066579-08/HL/NHLBI NIH HHS/ -- New York, N.Y. -- Science. 2009 Jul 24;325(5939):433. doi: 10.1126/science.1172447.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, WI 52336, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/19628861" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Base Sequence ; Dna ; Embryo, Mammalian ; Endodeoxyribonucleases/genetics/*metabolism ; Feasibility Studies ; Female ; *Gene Knockout Techniques ; Green Fluorescent Proteins ; Immunoglobulin M/*genetics ; Male ; *Microinjections ; Molecular Sequence Data ; Mutagenesis, Site-Directed ; RNA, Messenger ; Rats ; *Zinc Fingers/genetics ; rab GTP-Binding Proteins/*genetics
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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