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    APBC
    2008
    233views Bioinformatics» more  APBC 2008»
    Predicting Nucleolar Proteins Using Support-Vector Machines
    13 years 9 months ago
    Predicting Nucleolar Proteins Using Support-Vector Machines
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    Mikael Bodén
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    BMCBI
    2007
    101views more  BMCBI 2007»
    Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid
    13 years 7 months ago
    Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid
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    Takeyuki Tamura, Tatsuya Akutsu
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    BMCBI
    2008
    105views more  BMCBI 2008»
    Predicting RNA-binding sites of proteins using support vector machines and evolutionary information
    13 years 7 months ago
    Predicting RNA-binding sites of proteins using support vector machines and evolutionary information
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    Cheng-Wei Cheng, Emily Chia-Yu Su, Jenn-Kang Hwang...
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    BMCBI
    2010
    151views more  BMCBI 2010»
    Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and gene
    13 years 7 months ago
    Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and gene
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    Background: Because a priori knowledge about function of G protein-coupled receptors (GPCRs) can provide useful information to pharmaceutical research, the determination of their ...
    Zhanchao Li, Xuan Zhou, Zong Dai, Xiaoyong Zou
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    BMCBI
    2008
    140views more  BMCBI 2008»
    SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences
    13 years 7 months ago
    SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences
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    Background: Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologou...
    Lukasz A. Kurgan, Krzysztof J. Cios, Ke Chen 0003
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