Shenghan Lou

1.5k citations
49 papers · 885 indexed · 3 hit papers · h-index 16

Shenghan Lou

38 papers receiving 863 citations

Hit Papers

Metabolomic machine learning predictor for diagnosis and ...98202320262024202550100150200

Peers

Shenghan Lou
Comparison fields: 5 of 114
  • Orthopedics and Sports Medicine 118
  • Health Informatics 12
  • Oncology 230
  • Cancer Research 115
  • Molecular Biology 341
Replace Lianfang Huang with:
Lianfang Huang China
Young Jae Moon South Korea
Bing Chen China
Sílvia Carbonell Sala Italy
Wei Wan China
Jiaqin Cai China
Jianwei Wang China
Liang Zhu China
Zhiying Yue China
Shenghan Lou relative to Lianfang Huang China Lianfang Huang's profile →
Citations per field
00.5×4.3×
Lianfang Huang · 1×
Citations per year

Countries citing papers authored by Shenghan Lou

Since Specialization
Citations

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

Fields of papers citing papers by Shenghan Lou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 202517
2 20251
3 20250
4 20251
5 20251
6 20250
7 20253
8
Nanoparticles in tumor microenvironment remodeling and cancer immunotherapybreakdown →
2024220
9
Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancerbreakdown →
202498
10 20248
11 20222
12 202213
13 202216
14 202123
15 20205
16 201829
17 20187
18 20173
19 201615
20 201614

About Shenghan Lou

Shenghan Lou is a scholar working on Orthopedics and Sports Medicine, Oncology and Cancer Research, having authored 49 papers that have together received 885 indexed citations. Recurring topics across this work include Gastric Cancer Management and Outcomes (7 papers), RNA modifications and cancer (7 papers), Bone health and osteoporosis research (7 papers), Cancer-related molecular mechanisms research (6 papers), Radiomics and Machine Learning in Medical Imaging (6 papers), Bone health and treatments (5 papers), Bone fractures and treatments (5 papers) and Colorectal Cancer Screening and Detection (4 papers). The work is most often cited by research in Orthopedics and Sports Medicine (118 citations), Health Informatics (12 citations) and Oncology (230 citations). Shenghan Lou has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Peifu Tang, Houchen Lv, Zhirui Li, Licheng Zhang, Xin Yin, Peng Han, Yingwei Xue, Franklin R. Tay, Vinay Tergaonkar and Qiang Lü. Their work appears in journals such as Nature Communications, Journal of Clinical Oncology and Scientific Reports.

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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