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Bayesian Collective Markov Random Fields for Subcellular Localization Prediction[...]
4 Experimental results
4.1 Likelihood and prediction performance
2 The Bayesian Collective MRF Model
2.1 Markov Random Field (MRF) on protein SCL prediction
2.2 The weighted markov random field model
2.3 Gibbs sampler and likelihood estimation
2.4 Collective MRFs
3 Experimental setup
3.1 Dataset
3.2 Evaluation
3.3 Comparison partners
4 Experimental results
4.1 Likelihood and prediction performance
4.2 Effects of different potentials
4.3 A collective process improves the performance
4.4 Transductive learning from imbalanced MLDs
4.5 Comparison with existing methods
5 Conclusion and future work
Acknowledgments
References
Konferenzband
Bayesian Collective Markov Random Fields for Subcellular Localization Prediction of Human Proteins
Entstehung
2017
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