Edoardo M Airoldi

Summary

Affiliation: Princeton University
Country: USA

Publications

  1. pmc Getting started in probabilistic graphical models
    Edoardo M Airoldi
    Lewis Sigler Institute for Integrative Genomics and Computer Science Department, Princeton University, Princeton, New Jersey, United States of America
    PLoS Comput Biol 3:e252. 2007
  2. pmc Systems-level dynamic analyses of fate change in murine embryonic stem cells
    Rong Lu
    Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA
    Nature 462:358-62. 2009
  3. pmc Predicting cellular growth from gene expression signatures
    Edoardo M Airoldi
    Lewis Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, New Jersey, United States of America
    PLoS Comput Biol 5:e1000257. 2009
  4. pmc Aneuploidy prediction and tumor classification with heterogeneous hidden conditional random fields
    Zafer Barutcuoglu
    Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA
    Bioinformatics 25:1307-13. 2009
  5. ncbi request reprint A new machine learning classifier for high dimensional healthcare data
    Rema Padman
    The H John Heinz III School of Public Policy and Management, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Stud Health Technol Inform 129:664-8. 2007

Detail Information

Publications5

  1. pmc Getting started in probabilistic graphical models
    Edoardo M Airoldi
    Lewis Sigler Institute for Integrative Genomics and Computer Science Department, Princeton University, Princeton, New Jersey, United States of America
    PLoS Comput Biol 3:e252. 2007
  2. pmc Systems-level dynamic analyses of fate change in murine embryonic stem cells
    Rong Lu
    Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA
    Nature 462:358-62. 2009
    ....
  3. pmc Predicting cellular growth from gene expression signatures
    Edoardo M Airoldi
    Lewis Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, New Jersey, United States of America
    PLoS Comput Biol 5:e1000257. 2009
    ..Data and tools enabling others to apply our methods are available at http://function.princeton.edu/growthrate...
  4. pmc Aneuploidy prediction and tumor classification with heterogeneous hidden conditional random fields
    Zafer Barutcuoglu
    Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA
    Bioinformatics 25:1307-13. 2009
    ..Conversely, existing sequence classification methods can only model overall copy number instability, without regard to any particular position in the genome...
  5. ncbi request reprint A new machine learning classifier for high dimensional healthcare data
    Rema Padman
    The H John Heinz III School of Public Policy and Management, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Stud Health Technol Inform 129:664-8. 2007
    ..Furthermore, it has comparable or better prediction performance when compared against several machine learning methods, and provides insight into possible causal relations among the variables...