An Automated Combination of Kernels for Predicting Protein Subcellular Localization

@inproceedings{Ong2008AnAC,
  title={An Automated Combination of Kernels for Predicting Protein Subcellular Localization},
  author={Cheng Soon Ong and Alexander Zien},
  booktitle={WABI},
  year={2008}
}
Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions. We propose a new class of protein sequence kernels which considers all motifs including motifs with gaps. This class of kernels allows the inclusion of pairwise amino acid distances into their computation. We utilize an extension of the multiclass support vector machine (SVM) method which directly solves protein… CONTINUE READING
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Support vector machinebased method for subcellular localization of human proteins using amino acid composition , their order , and similarity search

M. Bhasin A. Garg, G. P. S. Raghava
The Journal of Biological Chemistry • 2005

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