LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone

@article{Chen2014LigandRFsRF,
  title={LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone},
  author={P. Chen and J. Huang and X. Gao},
  journal={BMC Bioinformatics},
  year={2014},
  volume={15},
  pages={S4 - S4}
}
  • P. Chen, J. Huang, X. Gao
  • Published 2014
  • Computer Science, Medicine, Biology
  • BMC Bioinformatics
  • BackgroundProtein-ligand binding is important for some proteins to perform their functions. Protein-ligand binding sites are the residues of proteins that physically bind to ligands. Despite of the recent advances in computational prediction for protein-ligand binding sites, the state-of-the-art methods search for similar, known structures of the query and predict the binding sites based on the solved structures. However, such structural information is not commonly available.ResultsIn this… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 42 REFERENCES
    Improved prediction of protein ligand-binding sites using random forests.
    16
    LIBRUS: combined machine learning and homology information for sequence-based ligand-binding residue prediction
    19
    Assessment of ligand-binding residue predictions in CASP9.
    65
    Prediction of zinc-binding sites in proteins from sequence
    95
    Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information
    37