Predicting fold novelty based on ProtoNet hierarchical classification

@article{Kifer2005PredictingFN,
  title={Predicting fold novelty based on ProtoNet hierarchical classification},
  author={Ilona Kifer and Ori Sasson and Michal Linial},
  journal={Bioinformatics},
  year={2005},
  volume={21 7},
  pages={
          1020-7
        }
}
MOTIVATION Structural genomics projects aim to solve a large number of protein structures with the ultimate objective of representing the entire protein space. The computational challenge is to identify and prioritize a small set of proteins with new, currently unknown, superfamilies or folds. RESULTS We develop a method that assigns each protein a likelihood of it belonging to a new, yet undetermined, structural superfamily. The method relies on a variant of ProtoNet, an automatic… CONTINUE READING

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