Junli Lu

61 papers receiving 675 citations

Junli Lu's Hit Papers

Myeloid-derived suppressor cells in cancer: therapeutic targets to overcome tumor immune evasion 2024 · 90 citations
900+1Years since publication255075

Peers

Junli Lu
Comparison fields: 5 of 124
  • Obstetrics and Gynecology 102
  • Signal Processing 50
  • Pediatrics, Perinatology and Child Health 75
  • Immunology 78
  • Transportation 24
Replace Yicong Li with:
Yicong Li China
Yuxi Li China
Elizabeth Cooper United States
Mohammad Sajjad Ghaemi Canada
Shabana Shabana Pakistan
Yongjin Wang China
Qiong Zhang China
Xiang Qiu China
Junli Lu relative to Yicong Li China Yicong Li's profile →
Citations per field
00.5×3.8×
Yicong Li · 1×
Citations per year

Countries citing papers authored by Junli Lu

Since Specialization
Citations

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

Fields of papers citing papers by Junli Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 66 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Myeloid-derived suppressor cells in cancer: therapeutic targets to overcome tumor immune evasion
Hit paper breakdown →
202490
2 202152
3 200951
4 201245
5 201339
6 201826
7 201924
8 201923
9 202123
10 201922
11 201619
12 201818
13 201615
14 201814
15 201512
16 201412
17 202112
18 202211
19 201611
20 202011

About Junli Lu

Junli Lu is a scholar working on Molecular Biology, Pediatrics, Perinatology and Child Health, Information Systems, Artificial Intelligence and Oncology, having authored 66 papers that have together received 694 indexed citations. Recurring topics across this work include Maternal and fetal healthcare (8 papers), Data Mining Algorithms and Applications (7 papers), Data Management and Algorithms (6 papers), Topic Modeling (3 papers), Rough Sets and Fuzzy Logic (3 papers), Natural Language Processing Techniques (3 papers), Ubiquitin and proteasome pathways (2 papers) and Pregnancy and preeclampsia studies (2 papers). The work is most often cited by research in Obstetrics and Gynecology (102 citations), Signal Processing (50 citations), Pediatrics, Perinatology and Child Health (75 citations), Immunology (78 citations) and Transportation (24 citations). Junli Lu has collaborated with scholars based in China, Japan and South Korea. Frequent co-authors include Yoichi Ochiai, Lizhen Wang, Dean Rao, Limin Xia, Tiantian Wang, Yiming Luo, Chai‐Hien Gan, Bixiang Zhang, Mi-Yen Yeh and Yiwei Li. Their work appears in journals such as BMC Pregnancy and Childbirth, International Journal of Gynecology & Obstetrics, SoftwareX, Cell Death and Disease and Biomarker Research.

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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