Vanathi Gopalakrishnan

Summary

Affiliation: University of Pittsburgh
Country: USA

Publications

  1. pmc 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
  2. pmc 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
  3. pmc 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
  4. pmc 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. pmc 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
  6. pmc 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
  7. pmc Context-sensitive markov models for peptide scoring and identification from tandem mass spectrometry
    Himanshu Grover
    Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania 15206 3701, USA
    OMICS 17:94-105. 2013
  8. pmc A multiplexed serum biomarker immunoassay panel discriminates clinical lung cancer patients from high-risk individuals found to be cancer-free by CT screening
    William L Bigbee
    Mass Spectrometry Platform, University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
    J Thorac Oncol 7:698-708. 2012
  9. ncbi request reprint 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
  10. pmc 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

Detail Information

Publications12

  1. pmc 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
    ..This article describes an approach that uses a Bayesian score to evaluate rule models...
  2. pmc 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...
  3. pmc 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...
  4. pmc 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. pmc 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...
  6. pmc 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...
  7. pmc Context-sensitive markov models for peptide scoring and identification from tandem mass spectrometry
    Himanshu Grover
    Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania 15206 3701, USA
    OMICS 17:94-105. 2013
    ..IO-HMMs offer a scalable and flexible framework with several modeling choices to learn complex patterns embedded in MS/MS data...
  8. pmc A multiplexed serum biomarker immunoassay panel discriminates clinical lung cancer patients from high-risk individuals found to be cancer-free by CT screening
    William L Bigbee
    Mass Spectrometry Platform, University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
    J Thorac Oncol 7:698-708. 2012
    ..Clinical decision making in the setting of computed tomography (CT) screening could benefit from accessible biomarkers that help predict the level of lung cancer risk in high-risk individuals with indeterminate pulmonary nodules...
  9. ncbi request reprint 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...
  10. pmc 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...
  11. ncbi request reprint 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...
  12. ncbi request reprint 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
    ..e. combining the scores or assignments from single or multiple prediction systems under the constraint of a whole sequence, as a target for improvement in protein secondary structure prediction...