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    » Prediction of Contact Maps Using Support Vector Machines
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    BMCBI
    2006
    106views more  BMCBI 2006»
    Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector
    13 years 7 months ago
    Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector
    Download  www.biomedcentral.com
    Metal-binding proteins play important roles in structural stability, signaling, regulation, transport, immune response, metabolism control, and metal homeostasis. Because of their...
    H. H. Lin, L. Y. Han, H. L. Zhang, C. J. Zheng, B....
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    PRICAI
    2004
    Springer
    131views Artificial Intelligence» more  PRICAI 2004»
    Prediction of the Risk Types of Human Papillomaviruses by Support Vector Machines
    14 years 21 days ago
    Prediction of the Risk Types of Human Papillomaviruses by Support Vector Machines
    Download  bi.snu.ac.kr
    Abstract. Infection by high-risk human papillomaviruses (HPVs) is associated with the development of cervical cancers. Classification of risk types is important to understand the ...
    Je-Gun Joung, Sok June Oh, Byoung-Tak Zhang
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    BMCBI
    2007
    127views more  BMCBI 2007»
    Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines
    13 years 7 months ago
    Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines
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    Background: Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occur approximately every 1000 bases in the overall human population. The no...
    Jian Tian, Ningfeng Wu, Xuexia Guo, Jun Guo, Juhua...
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    NLPRS
    2001
    Springer
    132views Natural Language Processing» more  NLPRS 2001»
    Unknown Word Guessing and Part-of-Speech Tagging Using Support Vector Machines
    13 years 11 months ago
    Unknown Word Guessing and Part-of-Speech Tagging Using Support Vector Machines
    Download  cl.aist-nara.ac.jp
    The accuracy of part-of-speech (POS) tagging for unknown words is substantially lower than that for known words. Considering the high accuracy rate of up-to-date statistical POS t...
    Tetsuji Nakagawa, Taku Kudo, Yuji Matsumoto
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    NAACL
    2007
    142views Computational Linguistics» more  NAACL 2007»
    Semi-Supervised Learning for Semantic Parsing using Support Vector Machines
    13 years 8 months ago
    Semi-Supervised Learning for Semantic Parsing using Support Vector Machines
    Download  www.cs.utexas.edu
    We present a method for utilizing unannotated sentences to improve a semantic parser which maps natural language (NL) sentences into their formal meaning representations (MRs). Gi...
    Rohit J. Kate, Raymond J. Mooney
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