Tinyi Chu

18 papers receiving 1.8k citations

Hit Papers

Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology 2022 · 242 citations
2422000202620082017200400600

Peers

Tinyi Chu
Comparison fields: 5 of 113
  • Biological Psychiatry 119
  • Neurology 364
  • Toxicology 149
  • Physiology 659
  • Pharmacology 348
Replace François Mouton‐Liger with:
François Mouton‐Liger France
A. Raquel Esteves Portugal
Kyong Nyon Nam United States
Usha Gundimeda United States
Yuemang Yao United States
Faraj Terro France
Masahiko Watabe Japan
Robert P. Strosznajder Poland
Ting-Hai Xu United States
Daniel José Barbosa Portugal
Tinyi Chu relative to François Mouton‐Liger France François Mouton‐Liger's profile →
Citations per field
00.5×4.4×
François Mouton‐Liger · 1×
Citations per year

Countries citing papers authored by Tinyi Chu

Since Specialization
Citations

This map shows the geographic impact of Tinyi Chu'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 Tinyi Chu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tinyi Chu more than expected).

Fields of papers citing papers by Tinyi Chu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tinyi Chu. 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 Tinyi Chu. The network helps show where Tinyi Chu may publish in the future.

Co-authors

The 25 scholars most cited alongside Tinyi Chu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tinyi Chu Line = papers co-authored together Tinyi Chu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1
Ibuprofen Suppresses Plaque Pathology and Inflammation in a Mouse Model for Alzheimer's Disease
Hit paper breakdown →
2000741
2
Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology
Hit paper breakdown →
2022242
3 1994193
4 2002122
5 2017107
6 201991
7 202280
8 201872
9 201858
10 202255
11 201843
12 201817
13 201913
14 201812
15 20185
16 20185
17 20192
18 20242
19 20250
20 20250

About Tinyi Chu

Tinyi Chu is a scholar working on Biological Psychiatry, Toxicology, Cancer Research, Neurology and Molecular Biology, having authored 20 papers that have together received 1.9k indexed citations. Recurring topics across this work include Genomics and Chromatin Dynamics (7 papers), RNA and protein synthesis mechanisms (5 papers), Alzheimer's disease research and treatments (3 papers), Genomics and Phylogenetic Studies (3 papers), RNA Research and Splicing (3 papers), RNA modifications and cancer (3 papers), Single-cell and spatial transcriptomics (2 papers) and Cancer Cells and Metastasis (2 papers). The work is most often cited by research in Biological Psychiatry (119 citations), Neurology (364 citations), Toxicology (149 citations), Physiology (659 citations) and Pharmacology (348 citations). Tinyi Chu has collaborated with scholars based in United States, China and Finland. Frequent co-authors include Charles G. Danko, Walter Beech, Oliver J. Ubeda, Greg M. Cole, Bruce Teter, Giselle P. Lim, Sally A. Frautschy, Zhong Wang, Pisin Chen and Thuy Tran. Their work appears in journals such as Nature Genetics, Nature Cancer, Cancer Research, Neurobiology of Disease and Journal of Neuroscience.

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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