Lude Franke

Molecular Biology · 2006–2019

A researcher in the Science corpus: 15 papers; trusts 6 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

Whom Lude Franke trusts overall

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

  1. Zemin Ning
  2. Minoru Kanehisa
  3. Katia Basso
  4. Homin K. Lee
  5. Adam A. Margolin
  6. Patrik D'haeseleer
  7. Anthony J. Cox
  8. Gerd Kempermann
  9. Ulrich Stelzl

Who trusts Lude Franke overall

  1. Trey Ideker
  2. Albert-László Barabási
  3. Oded Magger
  4. Xiujuan Wang
  5. Natali Gulbahce
  6. Roded Sharan
  7. Xuebing Wu

Papers from Lude Franke's perspective

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

  1. Using Bayesian Networks to Analyze Expression Data (Annual International Conference on Research in Computational Molecular Biology)
  2. A scaling normalization method for differential expression analysis of RNA-seq data (Genome Biology)
  3. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome (BMC Bioinformatics)
  4. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits (Proceedings of the National Academy of Sciences of the United States of America)
  5. A gene atlas of the mouse and human protein-encoding transcriptomes. (Proceedings of the National Academy of Sciences of the United States of America)
  6. Droplet barcoding for single cell transcriptomics applied to embryonic stem cells (Cell)
  7. Computational discovery of gene modules and regulatory networks (Nature Biotechnology)
  8. Computational and analytical challenges in single-cell transcriptomics (Nature reviews genetics)
  9. Singular value decomposition for genome-wide expression data processing and modeling. (Proceedings of the National Academy of Sciences of the United States of America)
  10. A General Framework for Weighted Gene Co-Expression Network Analysis (Statistical Applications in Genetics and Molecular Biology)

Their papers