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    » Scalable, updatable predictive models for sequence data
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    KDD
    2008
    ACM
    217views Data Mining» more  KDD 2008»
    Stream prediction using a generative model based on frequent episodes in event sequences
    14 years 8 months ago
    Stream prediction using a generative model based on frequent episodes in event sequences
    Download  research.microsoft.com
    This paper presents a new algorithm for sequence prediction over long categorical event streams. The input to the algorithm is a set of target event types whose occurrences we wis...
    Srivatsan Laxman, Vikram Tankasali, Ryen W. White
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    RECOMB
    2006
    Springer
    149views Computational Biology» more  RECOMB 2006»
    Improving Prediction of Zinc Binding Sites by Modeling the Linkage Between Residues Close in Sequence
    14 years 8 months ago
    Improving Prediction of Zinc Binding Sites by Modeling the Linkage Between Residues Close in Sequence
    Download  www.dsi.unifi.it
    Abstract. We describe and empirically evaluate machine learning methods for the prediction of zinc binding sites from protein sequences. We start by observing that a data set consi...
    Sauro Menchetti, Andrea Passerini, Paolo Frasconi,...
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    BIB
    2008
    86views more  BIB 2008»
    Recent developments in the MAFFT multiple sequence alignment program
    13 years 7 months ago
    Recent developments in the MAFFT multiple sequence alignment program
    Download  decapoda.nhm.org
    The accuracy and scalability of multiple sequence alignment (MSA) of DNAs and proteins have long been and are still important issues in bioinformatics. To rapidly construct a reas...
    Kazutaka Katoh, Hiroyuki Toh
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    FLAIRS
    2007
    147views Artificial Intelligence» more  FLAIRS 2007»
    Mining Sequences in Distributed Sensors Data for Energy Production
    13 years 10 months ago
    Mining Sequences in Distributed Sensors Data for Energy Production
    Download  www.aaai.org
    The desire to predict power generation at a given point in time is essential to power scheduling, energy trading, and availability modeling. The research conducted within is conce...
    Mehmed M. Kantardzic, John Gant
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    ISMB
    1996
    155views Computational Biology» more  ISMB 1996»
    Gene Recognition in Cyanobacterium Genomic Sequence Data Using the Hidden Markov Model
    13 years 9 months ago
    Gene Recognition in Cyanobacterium Genomic Sequence Data Using the Hidden Markov Model
    Download  www.aaai.org
    We have developed a hidden Markov model (HMM)to detect the protein coding regions within one megabase contiguous sequence data, registered in a database called GenBankin eight ent...
    Tetsushi Yada, Makoto Hirosawa
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