About: Extending the Ball-Histogram Method with Continuous Distributions and an Application to Prediction of DNA-Binding Proteins     Goto   Sponge   NotDistinct   Permalink

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Description
  • We introduce a novel method for prediction of DNA-binding propensity of proteins which extends our recently introduced ball-histogram method (Szabóová et al. 2012). Unlike the original ball-histogram method, it allows handling of continuous properties of protein regions. In experiments on four datasets of proteins, we show that the method improves upon the original ball-histogram method as well as other existing methods in terms of predictive accuracy.
  • We introduce a novel method for prediction of DNA-binding propensity of proteins which extends our recently introduced ball-histogram method (Szabóová et al. 2012). Unlike the original ball-histogram method, it allows handling of continuous properties of protein regions. In experiments on four datasets of proteins, we show that the method improves upon the original ball-histogram method as well as other existing methods in terms of predictive accuracy. (en)
Title
  • Extending the Ball-Histogram Method with Continuous Distributions and an Application to Prediction of DNA-Binding Proteins
  • Extending the Ball-Histogram Method with Continuous Distributions and an Application to Prediction of DNA-Binding Proteins (en)
skos:prefLabel
  • Extending the Ball-Histogram Method with Continuous Distributions and an Application to Prediction of DNA-Binding Proteins
  • Extending the Ball-Histogram Method with Continuous Distributions and an Application to Prediction of DNA-Binding Proteins (en)
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  • RIV/68407700:21230/12:00196256!RIV13-MSM-21230___
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  • P(GAP103/11/2170), P(ME10047), S
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  • 135902
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  • RIV/68407700:21230/12:00196256
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  • Relational Machine Learning; Prediction of DNA-binding Proteins (en)
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  • [557E26C81226]
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  • Philadelphia
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  • Los Alamitos
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  • Proceedings of 2012 IEEE International Conference on Bioinformatics and Biomedicine
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  • Kuželka, Ondřej
  • Szabóová, Andrea
  • Železný, Filip
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number of pages
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  • IEEE Computer Society
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  • 978-1-4673-2558-5
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  • 21230
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