Mo Huang

1.9k citations
40 papers · 1.1k indexed · 1 hit paper · h-index 14
Topics
Single-cell and spatial transcriptomics (5 papers)CRISPR and Genetic Engineering (4 papers)Cancer Immunotherapy and Biomarkers (4 papers)

In The Last Decade

Mo Huang

37 papers receiving 1.1k citations

Hit Papers

SAVER: gene expression recovery for single-cell RNA seque...20182026202020232018100200300400

Peers

Mo Huang
Comparison fields: 5 of 105
  • Molecular Biology 796
  • Cancer Research 212
  • Biomedical Engineering 169
  • Oncology 156
  • Immunology 126
Replace Xiao Tan with:
Xiao Tan Australia
Jerelyn Wong United States
Kok Siong Ang Singapore
Lassi Paavolainen Finland
Michael A. Tangrea United States
Qingyuan Zhu United States
Daniel Schraivogel Germany
Jianshe Yan China
Jessica A. Engel Australia
Mo Huang relative to Xiao Tan Australia Xiao Tan's profile →
Citations per field
00.5×20×40×65×
Xiao Tan · 1×
Citations per year

Countries citing papers authored by Mo Huang

Since Specialization
Citations

This map shows the geographic impact of Mo Huang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mo Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mo Huang more than expected).

Fields of papers citing papers by Mo Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mo Huang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mo Huang. The network helps show where Mo Huang may publish in the future.

Co-authorship network of co-authors of Mo Huang

This figure shows the co-authorship network connecting the top 25 collaborators of Mo Huang. A scholar is included among the top collaborators of Mo Huang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mo Huang. Mo Huang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 10
2 0
3 0
4 4
5 24
6 19
7 1
8 63
9 9
10
SAVER: gene expression recovery for single-cell RNA sequencingbreakdown →
458
11 42
12 3
13 11
14 13
15 148
16 13
17 7
18
Preliminary results on GPU Acceleration of the PIC Simulation Code OSIRIS Using CUDA
1
19 12
20 34

About Mo Huang

Mo Huang is a scholar working on Cancer Research, Oncology and Biophysics, having authored 40 papers that have together received 1.1k indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (5 papers), CRISPR and Genetic Engineering (4 papers) and Cancer Immunotherapy and Biomarkers (4 papers). The work is most often cited by research in Biophysics (105 citations), Cancer Research (212 citations) and Molecular Biology (796 citations). Mo Huang has collaborated with scholars based in United States, Singapore and China. Frequent co-authors include Nancy R. Zhang, Jingshu Wang, Mingyao Li, Sydney M. Shaffer, Eduardo A. Torre, Arjun Raj, Hannah Dueck, John I. Murray, Roberto Bonasio and Mo‐Huang Li. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Blood.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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