Mitchell E. Garber

Molecular Biology · 2001–2002

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Mitchell E. Garber trusts overall

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

  1. Wei Pan
  2. Anthony D. Long
  3. Pierre Baldi
  4. Sandrine Dudoit

Who trusts Mitchell E. Garber overall

  1. Ryota Suzuki
  2. Chris H. Q. Ding
  3. Olga G. Troyanskaya
  4. James Robert White

Papers from Mitchell E. Garber's perspective

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

  1. On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data (J. Comput. Biol.)
  2. STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS
  3. Improved Statistical Inference from DNA Microarray Data Using Analysis of Variance and A Bayesian Statistical Framework (Journal of Biological Chemistry)
  4. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments (Bioinform.)
  5. Genomic expression programs in the response of yeast cells to environmental changes. (Molecular Biology of the Cell)
  6. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling (Nature)
  7. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes (Bioinform.)
  8. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. (Molecular Biology of the Cell)
  9. Diversity of gene expression in adenocarcinoma of the lung (Proceedings of the National Academy of Sciences of the United States of America)
  10. Tissue Classification with Gene Expression Profiles (Annual International Conference on Research in Computational Molecular Biology)

Their papers