Chen Xu
Molecular Biology · with Su · 2015
A researcher in the Science corpus: 1 paper; trusts 11 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 13 citations into it; #2,858 of 17,773 by trust here, #10,193 by citations
- Artificial Intelligence — 6 citations into it
- Cancer Research — 5 citations into it
Whom Chen Xu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Daniel Ramsköld
- Levent Ertöz
- Antoine-Emmanuel Saliba
- S. Picelli
- Ron Shamir
- Michael S. Steinbach
- A. Westermann
- Rickard Sandberg
- J. Raser
- Vipin Kumar
Who trusts Chen Xu overall
- Rhonda L. Bacher
- Caleb Weinreb
- Peijie Lin
- Il-Youp Kwak
- Laleh Haghverdi
- F. Alexander Wolf
- Andrew W. Butler
- Minzhe Guo
- Christina Kendziorski
- Aaron T. L. Lun
Papers from Chen Xu's perspective
Ordered by how strongly the researchers Chen Xu trusts endorse each paper. Same corpus, a different order for every researcher.
- Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells
- Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells (Nature Structural &Molecular Biology)
- Smart-seq2 for sensitive full-length transcriptome profiling in single cells (Nature Methods)
- Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells (Nature)
- Full-Length mRNA-Seq from single cell levels of RNA and individual circulating tumor cells (Nature Biotechnology)
- Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. (Developmental Cell)
- Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data (SDM)
- CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. (Cell Reports)
- A clustering algorithm based on graph connectivity (Information Processing Letters)
- Single-cell RNA-seq: advances and future challenges (Nucleic Acids Research)
Their papers
- Identification of cell types from single-cell transcriptomes using a novel clustering method (2015) — 529 citations