Diabetes-Ling Wu

Molecular Biology · 2012–2015

A researcher in the Science corpus: 2 papers; trusts 14 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 48 citations into it; #3,828 of 17,773 by trust here, #2,679 by citations
• Genetics — 16 citations into it; #2,462 of 6,854 by trust here, #4,505 by citations
• Artificial Intelligence — 6 citations into it

Whom Diabetes-Ling Wu trusts overall

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

1. Gordon K. Smyth
2. Bradley Efron
3. Seon-Young Kim
4. Belinda Phipson
5. Robert Gentleman
6. Lu Tian
7. Robert Tibshirani
8. Aravind Subramanian
9. David J. Volsky
10. Mark D. Robinson

Who trusts Diabetes-Ling Wu overall

1. Yunshun Chen
2. Charity W. Law
3. Gordon K. Smyth
4. Matthew E. Ritchie
5. Aaron T. L. Lun
6. Greg Finak
7. Belinda Phipson
8. Wei Shi
9. Lizhong Chen
10. Jüri Reimand

Papers from Diabetes-Ling Wu's perspective

Ordered by how strongly the researchers Diabetes-Ling Wu 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. A scaling normalization method for differential expression analysis of RNA-seq data (Genome Biology)
3. From the Cover: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
4. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data (Bioinform.)
5. Sweave: Dynamic Generation of Statistical Reports Using Literate Data Analysis (International Conference on Computational Statistics)
6. Gene Ontology: tool for the unification of biology
7. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. (Nucleic Acids Research)
8. Aberrant luminal progenitors as the candidate target population for basal tumor development in BRCA1 mutation carriers (Nature Medicine)
9. STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS
10. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias (Bioinform.)

Their papers

• limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) — 34,728 citations
• Camera: a competitive gene set test accounting for inter-gene correlation (2012) — 802 citations