Vanathi Gopalakrishnan

Summary

Affiliation: University of Pittsburgh
Country: USA

Publications

  1. ncbi Application of an efficient Bayesian discretization method to biomedical data
    Jonathan L Lustgarten
    Department of Biomedical Informatics and the Intelligent Systems Program, University of Pittsburgh, Suite M 183 Vale, Parkvale Building, 200 Meyran Avenue, Pittsburgh, PA 15260, USA
    BMC Bioinformatics 12:309. 2011
  2. ncbi Knowledge-based variable selection for learning rules from proteomic data
    Jonathan L Lustgarten
    Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave, Parkvale M 183, Pittsburgh, PA, USA
    BMC Bioinformatics 10:S16. 2009
  3. ncbi Bayesian rule learning for biomedical data mining
    Vanathi Gopalakrishnan
    Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Avenue Suite M 183, Pittsburgh, PA 15260, USA
    Bioinformatics 26:668-75. 2010
  4. ncbi Discovery and verification of amyotrophic lateral sclerosis biomarkers by proteomics
    Henrik Ryberg
    Department of Pathology, University of Pittsburgh School of Medicine, BST S 420, 200 Lothrop Street, Pittsburgh, Pennsylvania 15261, USA
    Muscle Nerve 42:104-11. 2010
  5. ncbi Measuring stability of feature selection in biomedical datasets
    Jonathan L Lustgarten
    University of Pittsburgh Department of Biomedical Informatics, Pittsburgh, PA, USA
    AMIA Annu Symp Proc 2009:406-10. 2009
  6. ncbi Protein fold recognition using segmentation conditional random fields (SCRFs)
    Yan Liu
    School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    J Comput Biol 13:394-406. 2006
  7. ncbi Proteomic profiling of cerebrospinal fluid identifies biomarkers for amyotrophic lateral sclerosis
    Srikanth Ranganathan
    Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
    J Neurochem 95:1461-71. 2005
  8. ncbi Transfer learning of classification rules for biomarker discovery and verification from molecular profiling studies
    Philip Ganchev
    Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA
    J Biomed Inform 44:S17-23. 2011
  9. ncbi Machine-learning techniques for macromolecular crystallization data
    Vanathi Gopalakrishnan
    Intelligent Systems Laboratory, University of Pittsburgh, Pittsburgh, PA 15260, USA
    Acta Crystallogr D Biol Crystallogr 60:1705-16. 2004
  10. ncbi Comparison of probabilistic combination methods for protein secondary structure prediction
    Yan Liu
    Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA15213, USA
    Bioinformatics 20:3099-107. 2004

Collaborators

Detail Information

Publications10

  1. ncbi Application of an efficient Bayesian discretization method to biomedical data
    Jonathan L Lustgarten
    Department of Biomedical Informatics and the Intelligent Systems Program, University of Pittsburgh, Suite M 183 Vale, Parkvale Building, 200 Meyran Avenue, Pittsburgh, PA 15260, USA
    BMC Bioinformatics 12:309. 2011
    ..We compared the performance of EBD to Fayyad and Irani's (FI) discretization method, which is commonly used for discretization...
  2. ncbi Knowledge-based variable selection for learning rules from proteomic data
    Jonathan L Lustgarten
    Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave, Parkvale M 183, Pittsburgh, PA, USA
    BMC Bioinformatics 10:S16. 2009
    ..In particular, we use the Empirical Proteomics Ontology Knowledge Base (EPO-KB) that contains previously identified and validated proteomic biomarkers to select m/zs in a proteomic dataset prior to analysis to increase performance...
  3. ncbi Bayesian rule learning for biomedical data mining
    Vanathi Gopalakrishnan
    Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Avenue Suite M 183, Pittsburgh, PA 15260, USA
    Bioinformatics 26:668-75. 2010
    ..Moreover, BRL produces models that contain on average 70% fewer variables, which means that the biomarker panels for disease prediction contain fewer markers for further verification and validation by bench scientists...
  4. ncbi Discovery and verification of amyotrophic lateral sclerosis biomarkers by proteomics
    Henrik Ryberg
    Department of Pathology, University of Pittsburgh School of Medicine, BST S 420, 200 Lothrop Street, Pittsburgh, Pennsylvania 15261, USA
    Muscle Nerve 42:104-11. 2010
    ..CRP levels were increased in the CSF of ALS patients, and cystatin C level correlated with survival in patients with limb-onset disease. Our biomarker panel predicted ALS with an overall accuracy of 82%...
  5. ncbi Measuring stability of feature selection in biomedical datasets
    Jonathan L Lustgarten
    University of Pittsburgh Department of Biomedical Informatics, Pittsburgh, PA, USA
    AMIA Annu Symp Proc 2009:406-10. 2009
    ..We demonstrate the application of this measure on a biomedical dataset...
  6. ncbi Protein fold recognition using segmentation conditional random fields (SCRFs)
    Yan Liu
    School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    J Comput Biol 13:394-406. 2006
    ..Applying our prediction model to the Uniprot Database, we identify previously unknown potential beta-helices...
  7. ncbi Proteomic profiling of cerebrospinal fluid identifies biomarkers for amyotrophic lateral sclerosis
    Srikanth Ranganathan
    Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
    J Neurochem 95:1461-71. 2005
    ..We validated the SELDI-TOF-MS results for transthyretin and cystatin C by immunoblot and immunohistochemistry using commercially available antibodies. These findings identify a panel of CSF protein biomarkers for ALS...
  8. ncbi Transfer learning of classification rules for biomarker discovery and verification from molecular profiling studies
    Philip Ganchev
    Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA
    J Biomed Inform 44:S17-23. 2011
    ..It also improves performance over using the union of the data sets...
  9. ncbi Machine-learning techniques for macromolecular crystallization data
    Vanathi Gopalakrishnan
    Intelligent Systems Laboratory, University of Pittsburgh, Pittsburgh, PA 15260, USA
    Acta Crystallogr D Biol Crystallogr 60:1705-16. 2004
    ..Computational experiments indicate that such a system can retain and present informative rules pertaining to protein crystallization that warrant further confirmation via experimental techniques...
  10. ncbi Comparison of probabilistic combination methods for protein secondary structure prediction
    Yan Liu
    Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA15213, USA
    Bioinformatics 20:3099-107. 2004
    ..In particular, conditional random fields (CRFs) moderately improve the predictions for helices and, more importantly, for beta sheets, which are the major bottleneck for protein secondary structure prediction...