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    BMCBI
    2007
    179views more  BMCBI 2007»
    rMotifGen: random motif generator for DNA and protein sequences
    13 years 8 months ago
    rMotifGen: random motif generator for DNA and protein sequences
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    Background: Detection of short, subtle conserved motif regions within a set of related DNA or amino acid sequences can lead to discoveries about important regulatory domains such ...
    Eric C. Rouchka, C. Timothy Hardin
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    BMCBI
    2010
    189views more  BMCBI 2010»
    Efficient parallel and out of core algorithms for constructing large bi-directed de Bruijn graphs
    13 years 8 months ago
    Efficient parallel and out of core algorithms for constructing large bi-directed de Bruijn graphs
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    Background: Assembling genomic sequences from a set of overlapping reads is one of the most fundamental problems in computational biology. Algorithms addressing the assembly probl...
    Vamsi Kundeti, Sanguthevar Rajasekaran, Hieu Dinh,...
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    BMCBI
    2008
    111views more  BMCBI 2008»
    Probabilistic base calling of Solexa sequencing data
    13 years 8 months ago
    Probabilistic base calling of Solexa sequencing data
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    Background: Solexa/Illumina short-read ultra-high throughput DNA sequencing technology produces millions of short tags (up to 36 bases) by parallel sequencing-by-synthesis of DNA ...
    Jacques Rougemont, Arnaud Amzallag, Christian Isel...
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    BMCBI
    2007
    147views more  BMCBI 2007»
    Statistical analysis and significance testing of serial analysis of gene expression data using a Poisson mixture model
    13 years 8 months ago
    Statistical analysis and significance testing of serial analysis of gene expression data using a Poisson mixture model
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    Background: Serial analysis of gene expression (SAGE) is used to obtain quantitative snapshots of the transcriptome. These profiles are count-based and are assumed to follow a Bin...
    Scott D. Zuyderduyn
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    ROCAI
    2004
    Springer
    325views Artificial Intelligence» more  ROCAI 2004»
    Learning Mixtures of Localized Rules by Maximizing the Area Under the ROC Curve
    14 years 1 months ago
    Learning Mixtures of Localized Rules by Maximizing the Area Under the ROC Curve
    Download  www.mpi-inf.mpg.de
    We introduce a model class for statistical learning which is based on mixtures of propositional rules. In our mixture model, the weight of a rule is not uniform over the entire ins...
    Tobias Sing, Niko Beerenwinkel, Thomas Lengauer
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