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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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  • 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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  • 4
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    Unknown
    Translating dosage compensation to trisomy 21 (2013)
    Nature Publishing Group (NPG)
    Details
    Publication Date: 2013-07-19
    Description: Down's syndrome is a common disorder with enormous medical and social costs, caused by trisomy for chromosome 21. We tested the concept that gene imbalance across an extra chromosome can be de facto corrected by manipulating a single gene, XIST (the X-inactivation gene). Using genome editing with zinc finger nucleases, we inserted a large, inducible XIST transgene into the DYRK1A locus on chromosome 21, in Down's syndrome pluripotent stem cells. The XIST non-coding RNA coats chromosome 21 and triggers stable heterochromatin modifications, chromosome-wide transcriptional silencing and DNA methylation to form a 'chromosome 21 Barr body'. This provides a model to study human chromosome inactivation and creates a system to investigate genomic expression changes and cellular pathologies of trisomy 21, free from genetic and epigenetic noise. Notably, deficits in proliferation and neural rosette formation are rapidly reversed upon silencing one chromosome 21. Successful trisomy silencing in vitro also surmounts the major first step towards potential development of 'chromosome therapy'.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3848249/" 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/PMC3848249/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Jiang, Jun -- Jing, Yuanchun -- Cost, Gregory J -- Chiang, Jen-Chieh -- Kolpa, Heather J -- Cotton, Allison M -- Carone, Dawn M -- Carone, Benjamin R -- Shivak, David A -- Guschin, Dmitry Y -- Pearl, Jocelynn R -- Rebar, Edward J -- Byron, Meg -- Gregory, Philip D -- Brown, Carolyn J -- Urnov, Fyodor D -- Hall, Lisa L -- Lawrence, Jeanne B -- 1F32CA154086/CA/NCI NIH HHS/ -- 2T32HD007439/HD/NICHD NIH HHS/ -- F32 CA154086/CA/NCI NIH HHS/ -- GM053234/GM/NIGMS NIH HHS/ -- GM085548/GM/NIGMS NIH HHS/ -- GM096400 RC4/GM/NIGMS NIH HHS/ -- MOP-13680/Canadian Institutes of Health Research/Canada -- R01 GM053234/GM/NIGMS NIH HHS/ -- R01 GM085548/GM/NIGMS NIH HHS/ -- RC4 GM096400/GM/NIGMS NIH HHS/ -- T32 HD007439/HD/NICHD NIH HHS/ -- England -- Nature. 2013 Aug 15;500(7462):296-300. doi: 10.1038/nature12394. Epub 2013 Jul 17.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Cell and Developmental Biology, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, Massachusetts 01655, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/23863942" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Cell Line ; Cell Proliferation ; Chromosomes, Human, Pair 21/*genetics ; DNA Methylation ; *Dosage Compensation, Genetic ; Down Syndrome/*genetics/therapy ; Gene Silencing ; Humans ; Induced Pluripotent Stem Cells ; Male ; Mice ; Mutagenesis, Insertional ; Neurogenesis ; RNA, Long Noncoding/genetics/*metabolism ; Sex Chromatin/genetics ; X Chromosome Inactivation/genetics
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Published by Nature Publishing Group (NPG)
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