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    APBC
    2003
    116views Bioinformatics» more  APBC 2003»
    An Empirical Comparison of Supervised Machine Learning Techniques in Bioinformatics
    13 years 9 months ago
    An Empirical Comparison of Supervised Machine Learning Techniques in Bioinformatics
    Download  www.acs.org.au
    Research in bioinformatics is driven by the experimental data. Current biological databases are populated by vast amounts of experimental data. Machine learning has been widely ap...
    Aik Choon Tan, David Gilbert
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    ESANN
    2006
    106views Neural Networks» more  ESANN 2006»
    Using sampling methods to improve binding site predictions
    13 years 9 months ago
    Using sampling methods to improve binding site predictions
    Download  www.dice.ucl.ac.be
    Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. In previous work we combine random selection under-sampling with th...
    Yi Sun, Mark Robinson, Rod Adams, Rene te Boekhors...
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    ICMLA
    2008
    145views Machine Learning» more  ICMLA 2008»
    Tumor Targeting for Lung Cancer Radiotherapy Using Machine Learning Techniques
    13 years 9 months ago
    Tumor Targeting for Lung Cancer Radiotherapy Using Machine Learning Techniques
    Download  www.cis.pku.edu.cn
    Accurate lung tumor targeting in real time plays a fundamental role in image-guide radiotherapy of lung cancers. Precise tumor targeting is required for both respiratory gating an...
    Tong Lin, Laura Cervino, Xiaoli Tang, Nuno Vasconc...
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    BMCBI
    2008
    141views more  BMCBI 2008»
    Functional discrimination of membrane proteins using machine learning techniques
    13 years 7 months ago
    Functional discrimination of membrane proteins using machine learning techniques
    Download  www.biomedcentral.com
    Background: Discriminating membrane proteins based on their functions is an important task in genome annotation. In this work, we have analyzed the characteristic features of amin...
    M. Michael Gromiha, Yukimitsu Yabuki
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    SAT
    2009
    Springer
    111views Hardware» more  SAT 2009»
    Restart Strategy Selection Using Machine Learning Techniques
    14 years 2 months ago
    Restart Strategy Selection Using Machine Learning Techniques
    Download  www.cse.unsw.edu.au
    Abstract. Restart strategies are an important factor in the performance of conflict-driven Davis Putnam style SAT solvers. Selecting a good restart strategy for a problem instance...
    Shai Haim, Toby Walsh
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