Hannah Kim

Molecular Biology · 2017

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

Whom Hannah Kim trusts overall

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

  1. Emma Pierson
  2. David Arthur

Who trusts Hannah Kim overall

  1. Romain Lopez

Papers from Hannah Kim's perspective

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

  1. Learning a similarity metric discriminatively, with application to face verification
  2. Why Does Unsupervised Pre-training Help Deep Learning?
  3. Multilayer feedforward networks are universal approximators
  4. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
  5. Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance
  6. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing (Nature Neuroscience)
  7. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells (Nature Biotechnology)
  8. ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis (Genome Biology)
  9. Droplet barcoding for single cell transcriptomics applied to embryonic stem cells (Cell)
  10. Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data (Machine-mediated learning)

Their papers