William T. Barry

Molecular Biology · with Abkevich · 2005–2016

A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

Whom William T. Barry trusts overall

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

  1. Yoav Benjamini
  2. John D. Storey
  3. Sandrine Dudoit
  4. Y. H. Yang
  5. Fátima Al-Shahrour
  6. Michael A. Newton
  7. Y. Hochberg
  8. Robert Tibshirani
  9. Brigitte Boeckmann
  10. Minoru Kanehisa

Who trusts William T. Barry overall

  1. Songxi Chen
  2. Jianguo Xia
  3. Sonja Hänzelmann
  4. Purvesh Khatri
  5. M. Kallio
  6. David S. Wishart
  7. Yingli Qin
  8. Robert Castelo
  9. Marina Sirota
  10. Jun Yu Li

Papers from William T. Barry's perspective

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

  1. Controlling the false discovery rate: a practical and powerful approach to multiple testing
  2. STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS
  3. Statistical significance for genomewide studies (Proceedings of the National Academy of Sciences of the United States of America)
  4. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. (Nucleic Acids Research)
  5. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes (Bioinform.)
  6. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses (Proceedings of the National Academy of Sciences of the United States of America)
  7. Gene-expression profiles predict survival of patients with lung adenocarcinoma (Nature Medicine)
  8. A global test for groups of genes: testing association with a clinical outcome (Bioinform.)
  9. GOstat: find statistically overrepresented Gene Ontologies within a group of genes. (Bioinformatics)
  10. GoMiner: a resource for biological interpretation of genomic and proteomic data (Genome Biology)

Their papers