Tianda Li

1.9k citations
29 papers · 853 · h-index 15

Impact in

Papers in

    • Pluripotent Stem Cells Research 16
    • CRISPR and Genetic Engineering 15
    • Renal and related cancers 4
    • Epigenetics and DNA Methylation 3
    • Animal Genetics and Reproduction 6

Tianda Li

28 papers receiving 836 citations

Peers

Tianda Li
Comparison fields: 5 of 82
  • Business and International Management 30
  • Aging 23
  • Molecular Biology 651
  • Genetics 194
  • Reproductive Medicine 48
Replace Daisuke Mashiko with:
Daisuke Mashiko Japan
Annekatrien Boel Belgium
Quanjun Zhang China
Nada Kubikova United Kingdom
Mandana Arbab United States
Xiaoling Yi China
Chunwei Zheng China
Lanzhen Yan China
A Modzelewski United States
Tianda Li relative to Daisuke Mashiko Japan Daisuke Mashiko's profile →
Citations per field
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Daisuke Mashiko · 1×
Citations per year

Countries citing papers authored by Tianda Li

Since Specialization
Citations

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

Fields of papers citing papers by Tianda Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Tianda Li, 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 Tianda Li Line = papers co-authored together Tianda Li links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018200
2 2012132
3 202080
4 201378
5 202057
6 202042
7 201640
8 201932
9 201929
10 201727
11 201518
12 201617
13 201717
14 201915
15 201715
16 201310
17 20129
18 20217
19 20127
20 20215

About Tianda Li

Tianda Li is a scholar working on Molecular Biology, Genetics, Artificial Intelligence, Public Health, Environmental and Occupational Health and Surgery, having authored 29 papers that have together received 853 indexed citations. Recurring topics across this work include Pluripotent Stem Cells Research (16 papers), CRISPR and Genetic Engineering (15 papers), Animal Genetics and Reproduction (6 papers), Topic Modeling (6 papers), Natural Language Processing Techniques (4 papers), Renal and related cancers (4 papers), Reproductive Biology and Fertility (4 papers) and Epigenetics and DNA Methylation (3 papers). The work is most often cited by research in Business and International Management (30 citations), Aging (23 citations), Molecular Biology (651 citations), Genetics (194 citations) and Reproductive Medicine (48 citations). Tianda Li has collaborated with scholars based in China, Canada and Sweden. Frequent co-authors include Qi Zhou, Guihai Feng, Kai Xu, Lu Guo, Tongtong Cui, Qingqin Gao, Haifeng Wan, Fei Teng, Wei Li and Jing Li. Their work appears in journals such as Cell Reports, Journal of genetics and genomics, Scientific Reports, Protein & Cell and Frontiers in Cell and Developmental Biology.

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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