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  • 1
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    Boca Raton : CRC Press, Taylor & Francis Group
    Call number: QP620:150
    Keywords: Sequence Analysis, RNA / methods ; Transcriptome ; Statistics as Topic
    Abstract: "RNA-seq offers unprecedented information about transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. This self-contained guide enables researchers to examine differential expression at gene, exon, and transcript level and to discover novel genes, transcripts, and whole transcriptomes. Each chapter starts with theoretical background, followed by descriptions of relevant analysis tools. The book also provides examples using command line tools and the R statistical environment. For non-programming scientists, the same examples are covered using open source software with a graphical user interface"--
    Notes: "A Chapman & Hall book."
    Pages: xxiv, 298 pages : illustrations
    ISBN: 9781466595002
    Signatur Availability
    QP620:150 on loan
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  • 2
    ISSN: 1573-773X
    Keywords: music retrieval ; redundant hash addressing ; self-organizing map ; sequence processing ; speech recognition
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Kohonen's Self-Organizing Map (SOM) is combined with the Redundant Hash Addressing (RHA) principle. The SOM encodes the input feature vector sequence into the sequence of best-matching unit (BMU) indices and the RHA principle is then used to associate the BMU index sequence with the dictionary items. This provides a fast alternative for dynamic programming (DP) based methods for comparing and matching temporal sequences. Experiments include music retrieval and speech recognition. The separation of the classes can be improved by error-corrective learning. Comparisons to DP-based methods are presented.
    Type of Medium: Electronic Resource
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  • 3
    ISSN: 1573-773X
    Keywords: learning vector quantization ; self-organizing map ; sequence processing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The Self-Organizing Map (SOM) and Learning Vector Quantization (LVQ) algorithms are constructed in this work for variable-length and warped feature sequences. The novelty is to associate an entire feature vector sequence, instead of a single feature vector, as a model with each SOM node. Dynamic time warping is used to obtain time-normalized distances between sequences with different lengths. Starting with random initialization, ordered feature sequence maps then ensue, and Learning Vector Quantization can be used to fine tune the prototype sequences for optimal class separation. The resulting SOM models, the prototype sequences, can then be used for the recognition as well as synthesis of patterns. Good results have been obtained in speaker-independent speech recognition.
    Type of Medium: Electronic Resource
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