Yi‐Chang Liu

3.1k citations
123 papers · 2.0k indexed · h-index 24

Yi‐Chang Liu

119 papers receiving 2.0k citations

Peers

Yi‐Chang Liu
Comparison fields: 5 of 127
  • Process Chemistry and Technology 286
  • Endocrine and Autonomic Systems 210
  • Aging 45
  • Biomaterials 302
  • Cancer Research 311
Replace Pascal Loyer with:
Pascal Loyer France
Yuanhua Liu China
Zhilong Chen China
Yoshiro Kobayashi Japan
Akio Hoshi Japan
Hidetoshi Yamanaka Japan
Wenjun Wang China
Ke Chen China
Shi Yang United States
Yi‐Chang Liu relative to Pascal Loyer France Pascal Loyer's profile →
Citations per field
00.5×10×15×17.5×
Pascal Loyer · 1×
Citations per year

Countries citing papers authored by Yi‐Chang Liu

Since Specialization
Citations

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

Fields of papers citing papers by Yi‐Chang Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yi‐Chang Liu. 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 Yi‐Chang Liu. The network helps show where Yi‐Chang Liu may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20235
2 20232
3 20231
4 20232
5 20235
6 20224
7 202210
8 20227
9 201813
10 201845
11 201634
12
Up-regulation of PER3 Expression Is Correlated with Better Clinical Outcome in Acute Leukemia.
201522
13 201520
14 201338
15 201113
16 20095
17 2006103
18 20069
19 20044
20
Polynomial Algorithms for Weighted Perfect Domination Problems on Interval and Circular-Arc Graphs.
19948

About Yi‐Chang Liu

Yi‐Chang Liu is a scholar working on Process Chemistry and Technology, Hematology and Toxicology, having authored 123 papers that have together received 2.0k indexed citations. Recurring topics across this work include Lymphoma Diagnosis and Treatment (15 papers), Acute Myeloid Leukemia Research (12 papers), Chronic Lymphocytic Leukemia Research (9 papers), Chronic Myeloid Leukemia Treatments (9 papers), Viral-associated cancers and disorders (8 papers), Carbon dioxide utilization in catalysis (7 papers), Bioactive Compounds and Antitumor Agents (6 papers) and biodegradable polymer synthesis and properties (6 papers). The work is most often cited by research in Process Chemistry and Technology (286 citations), Endocrine and Autonomic Systems (210 citations) and Aging (45 citations). Yi‐Chang Liu has collaborated with scholars based in Taiwan, Egypt and United States. Frequent co-authors include Bao‐Tsan Ko, Hui‐Hua Hsiao, Chu‐Chieh Lin, Ming‐Yu Yang, Pai‐Mei Lin, Sheng-Fung Lin, Sheng‐Fung Lin, Ta‐Chih Liu, Shih‐Feng Cho and Wen‐Chi Yang. Their work appears in journals such as Scientific Reports, Marine Drugs, International Journal of Molecular Sciences, Journal of Personalized Medicine and Journal of Clinical Oncology.

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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