M. Sabripour

Molecular Biology · 2006

A researcher in the Science corpus: 1 paper; trusts 7 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

Whom M. Sabripour trusts

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Terence P. Speed (in Molecular Biology)
  2. Anthony D. Long (in Molecular Biology)
  3. Pierre Baldi (in Molecular Biology)
  4. Stephan W. Morris (in Molecular Biology)
  5. S. Pounds (in Molecular Biology)
  6. Rafael A. Irizarry (in Molecular Biology)
  7. Benjamin M. Bolstad (in Molecular Biology)

Who trusts M. Sabripour

  1. Susan P. Holmes (in Molecular Biology)
  2. Paul J. McMurdie (in Molecular Biology)
  3. Joseph R. Nevins (in Molecular Biology)
  4. Jeffrey T. Chang (in Molecular Biology)

Papers from M. Sabripour's perspective

Ordered by how strongly the researchers M. Sabripour trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Empirical Bayes Analysis of a Microarray Experiment
  2. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias (Bioinform.)
  3. Summaries of Affymetrix GeneChip probe level data. (Nucleic Acids Research)
  4. On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data (J. Comput. Biol.)
  5. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes (Bioinform.)
  6. Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments (Proceedings of the National Academy of Sciences of the United States of America)
  7. Selection bias in gene extraction on the basis of microarray gene-expression data (Proceedings of the National Academy of Sciences of the United States of America)
  8. A mixture model approach for the analysis of microarray gene expression data
  9. Validating clustering for gene expression data (Bioinform.)
  10. Detecting differential gene expression with a semiparametric hierarchical mixture method. (Biostatistics)

Their papers