Hon Nian Chua

Summary

Country: Singapore

Publications

  1. ncbi Using indirect protein interactions for the prediction of Gene Ontology functions
    Hon Nian Chua
    School of Computing, National University of Singapore, Singapore, Singapore
    BMC Bioinformatics 8:S8. 2007
  2. ncbi An efficient strategy for extensive integration of diverse biological data for protein function prediction
    Hon Nian Chua
    Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
    Bioinformatics 23:3364-73. 2007
  3. ncbi A probabilistic graph-theoretic approach to integrate multiple predictions for the protein-protein subnetwork prediction challenge
    Hon Nian Chua
    Data Mining Department, Institute for Infocomm Research, Singapore
    Ann N Y Acad Sci 1158:224-33. 2009
  4. ncbi Using indirect protein-protein interactions for protein complex prediction
    Hon Nian Chua
    Graduate School of Integrated Sciences, National University of Singapore, Singapore
    J Bioinform Comput Biol 6:435-66. 2008
  5. ncbi Complex discovery from weighted PPI networks
    Guimei Liu
    School of Computing, National University of Singapore, Singapore and Institute for Infocomm Research, Singapore
    Bioinformatics 25:1891-7. 2009
  6. ncbi Using indirect protein-protein interactions for protein complex predication
    Hon Nian Chua
    Graduate School of Integrated Sciences, National University of Singapore, Singapore
    Comput Syst Bioinformatics Conf 6:97-109. 2007
  7. ncbi Increasing the reliability of protein interactomes
    Hon Nian Chua
    Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore
    Drug Discov Today 13:652-8. 2008
  8. ncbi Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions
    Hon Nian Chua
    Graduate School for Integrated Sciences and Engineering, National University of Singapore, Singapore
    Bioinformatics 22:1623-30. 2006
  9. ncbi Increasing confidence of protein-protein interactomes
    Jin Chen
    National University of Singapore, Graduate School of Computing, Singapore
    Genome Inform 17:284-97. 2006
  10. ncbi Supervised maximum-likelihood weighting of composite protein networks for complex prediction
    Chern Han Yong
    Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
    BMC Syst Biol 6:S13. 2012

Detail Information

Publications11

  1. ncbi Using indirect protein interactions for the prediction of Gene Ontology functions
    Hon Nian Chua
    School of Computing, National University of Singapore, Singapore, Singapore
    BMC Bioinformatics 8:S8. 2007
    ....
  2. ncbi An efficient strategy for extensive integration of diverse biological data for protein function prediction
    Hon Nian Chua
    Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
    Bioinformatics 23:3364-73. 2007
    ..In the presence of cross-genome information, which is overwhelming for existing approaches, IWA makes even better predictions. We also demonstrate the significance of appropriate weighting strategies in data integration...
  3. ncbi A probabilistic graph-theoretic approach to integrate multiple predictions for the protein-protein subnetwork prediction challenge
    Hon Nian Chua
    Data Mining Department, Institute for Infocomm Research, Singapore
    Ann N Y Acad Sci 1158:224-33. 2009
    ..This technique is then used to produce our entry for the protein-protein subnetwork prediction challenge...
  4. ncbi Using indirect protein-protein interactions for protein complex prediction
    Hon Nian Chua
    Graduate School of Integrated Sciences, National University of Singapore, Singapore
    J Bioinform Comput Biol 6:435-66. 2008
    ..Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes...
  5. ncbi Complex discovery from weighted PPI networks
    Guimei Liu
    School of Computing, National University of Singapore, Singapore and Institute for Infocomm Research, Singapore
    Bioinformatics 25:1891-7. 2009
    ..However, protein interaction data produced by high-throughput experiments are often associated with high false positive and false negative rates, which makes it difficult to predict complexes accurately...
  6. ncbi Using indirect protein-protein interactions for protein complex predication
    Hon Nian Chua
    Graduate School of Integrated Sciences, National University of Singapore, Singapore
    Comput Syst Bioinformatics Conf 6:97-109. 2007
    ..Since no any other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes...
  7. ncbi Increasing the reliability of protein interactomes
    Hon Nian Chua
    Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore
    Drug Discov Today 13:652-8. 2008
    ..We review here computational techniques for assessing and improving the reliability of protein-protein interaction data from these high-throughput experiments...
  8. ncbi Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions
    Hon Nian Chua
    Graduate School for Integrated Sciences and Engineering, National University of Singapore, Singapore
    Bioinformatics 22:1623-30. 2006
    ..Using leave-one-out cross validation, we compare the performance of our method against that of several other existing approaches and show that our method performs relatively well...
  9. ncbi Increasing confidence of protein-protein interactomes
    Jin Chen
    National University of Singapore, Graduate School of Computing, Singapore
    Genome Inform 17:284-97. 2006
    ..We also present indices that are based on explicit motifs associated with true-positive protein interactions--e.g., "new interaction generality" (IG2) and "meso-scale motifs" (NeMoFinder)...
  10. ncbi Supervised maximum-likelihood weighting of composite protein networks for complex prediction
    Chern Han Yong
    Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
    BMC Syst Biol 6:S13. 2012
    ..As a result, predicted complexes often do not match true complexes well, and many true complexes go undetected...
  11. ncbi Decomposing PPI networks for complex discovery
    Guimei Liu
    School of Computing, National University of Singapore, Singapore
    Proteome Sci 9:S15. 2011
    ..As a result, predicted complexes often contain many spuriously included proteins, precluding them from matching true complexes...