Johann A. Gagnon-Bartsch

Molecular Biology · 2012–2015

A researcher in the Science corpus: 2 papers; trusts 16 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 28 citations into it; #2,142 of 17,773 by trust here, #10,864 by citations
• Genetics — 6 citations into it
• Statistics and Probability — 5 citations into it

Whom Johann A. Gagnon-Bartsch trusts overall

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

1. Gordon K. Smyth
2. Rafael A. Irizarry
3. Radford M. Neal
4. Hyun Min Kang
5. Benjamin M. Bolstad
6. Jeffrey T. Leek
7. William N. Venables
8. Terence P. Speed
9. John D. Storey
10. B. D. Ripley

Who trusts Johann A. Gagnon-Bartsch overall

1. Jeffrey T. Leek
2. Matthew Stephens
3. Andrew E. Jaffe
4. Davide Risso
5. Charlotte Soneson
6. M. van Iterson
7. Allon Wagner
8. Andrew E. Teschendorff
9. Simon Anders
10. Florian Buettner

Papers from Johann A. Gagnon-Bartsch's perspective

Ordered by how strongly the researchers Johann A. Gagnon-Bartsch trusts endorse each paper. Same corpus, a different order for every researcher.

1. Singular value decomposition for genome-wide expression data processing and modeling. (Proceedings of the National Academy of Sciences of the United States of America)
2. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (Statistical Applications in Genetics and Molecular Biology)
3. Pattern Recognition and Machine Learning
4. Efficient Control of Population Structure in Model Organism Association Mapping (Genetics)
5. A general framework for multiple testing dependence
6. Summaries of Affymetrix GeneChip probe level data. (Nucleic Acids Research)
7. Normalization of cDNA microarray data. (Methods)
8. Variance component model to account for sample structure in genome-wide association studies (Nature Genetics)
9. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias (Bioinform.)
10. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements (Nature Biotechnology)

Their papers

• Using control genes to correct for unwanted variation in microarray data. (2012) — 439 citations
• Systematic noise degrades gene co-expression signals but can be corrected (2015) — 55 citations