Bicheng Ye
Impact in
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- Cancer-related molecular mechanisms research
- Cancer Genomics and Diagnostics
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- Ferroptosis and cancer prognosis
Papers in
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- Single-cell and spatial transcriptomics 3
- Bioinformatics and Genomic Networks 1
- Oncology 6
- Cancer Immunotherapy and Biomarkers 5
- Inflammatory Biomarkers in Disease Prognosis 1
- Co-authors
- Songyun Zhao (5 shared papers)Chao Cheng (3 shared papers)Jinhui Liu (3 shared papers)Pengpeng Zhang (4 shared papers)Lanyu Wang (1 shared paper)Jianfeng Shao (1 shared paper)Aimin Jiang (3 shared papers)Jiaheng Xie (2 shared papers)
In The Last Decade
Bicheng Ye
17 papers receiving 349 citations
Bicheng Ye's Hit Papers
Peers
Comparison fields: 5 of 39
- Cancer Research 65
- Pulmonary and Respiratory Medicine 111
- Oncology 65
- Immunology 42
- Molecular Biology 110
Countries citing papers authored by Bicheng Ye
This map shows the geographic impact of Bicheng Ye'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 Bicheng Ye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bicheng Ye more than expected).
Fields of papers citing papers by Bicheng Ye
This network shows the impact of papers produced by Bicheng Ye. 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 Bicheng Ye. The network helps show where Bicheng Ye may publish in the future.
Co-authors
The 25 scholars most cited alongside Bicheng Ye, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework Hit paper breakdown → | 2023 | 100 |
| 2 | iMLGAM: Integrated Machine Learning and Genetic Algorithm‐driven Multiomics analysis for pan‐cancer immunotherapy response prediction Hit paper breakdown → | 2025 | 70 |
| 3 | 2024 | 32 | |
| 4 | 2023 | 29 | |
| 5 | 2023 | 26 | |
| 6 | 2023 | 20 | |
| 7 | 2023 | 17 | |
| 8 | 2024 | 12 | |
| 9 | 2024 | 12 | |
| 10 | 2023 | 11 | |
| 11 | 2024 | 10 | |
| 12 | 2024 | 5 | |
| 13 | 2022 | 4 | |
| 14 | 2022 | 3 | |
| 15 | 2025 | 1 | |
| 16 | 2025 | 1 | |
| 17 | 2014 | 1 | |
| 18 | 2025 | 0 | |
| 19 | 2024 | 0 |
About Bicheng Ye
Bicheng Ye is a scholar working on Molecular Biology, Oncology, Cancer Research, Pulmonary and Respiratory Medicine and Immunology, having authored 19 papers that have together received 354 indexed citations. Recurring topics across this work include Cancer Genomics and Diagnostics (5 papers), Cancer Immunotherapy and Biomarkers (5 papers), Ferroptosis and cancer prognosis (3 papers), Single-cell and spatial transcriptomics (3 papers), Renal cell carcinoma treatment (2 papers), Immunotherapy and Immune Responses (2 papers), Inflammatory Biomarkers in Disease Prognosis (1 paper) and Bioinformatics and Genomic Networks (1 paper). The work is most often cited by research in Cancer Research (65 citations), Pulmonary and Respiratory Medicine (111 citations), Oncology (65 citations), Immunology (42 citations) and Molecular Biology (110 citations). Bicheng Ye has collaborated with scholars based in China and Hong Kong. Frequent co-authors include Songyun Zhao, Chao Cheng, Jinhui Liu, Pengpeng Zhang, Lanyu Wang, Jianfeng Shao, Aimin Jiang, Jiaheng Xie, Hao Chi and Chuanli Ren. Their work appears in journals such as npj Digital Medicine, Frontiers in Immunology, Translational Oncology, BioFactors and Cancer Biology & Therapy.
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.