Minfeng Chen
Biographic Data
| ID | 10384133 |
|---|---|
| NAME | Minfeng Chen |
| GIVEN NAMES | Minfeng |
| FAMILY NAME | Chen |
| SIGNATURE | CHEN M |
| AFFILIATIONS | BGI Group (China) |
| ORCID | 0000-0001-5185-3917 |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2010 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Integrating large language models into prostate cancer training
BACKGROUND: Large language models (LLMs) are increasingly explored as tools for medical education. However, evidence remains limited regarding their pedagogical quality and real-world utility in prostate cancer teaching within urology residency training. METHODS: We conducted a two-phase study. Phase 1 benchmarked three LLMs (ChatGPT-4o, DeepSeek R1, and Gemini 2.0) on a structured 40-item prostate cancer education question bank using standardize…
Ancient human genome sequence of an extinct Palaeo-Eskimo
We report here the genome sequence of an ancient human. Obtained from ∼4,000-year-old permafrost-preserved hair, the genome represents a male individual from the first known culture to settle in Greenland. Sequenced to an average depth of 20×, we recover 79% of the diploid genome, an amount close to the practical limit of current sequencing technologies. We identify 353,151 high-confidence single-nucleotide polymorphisms (SNPs), of which 6.8% hav…
No prominent works on this page.
Ancient human genome sequence of an extinct Palaeo-Eskimo
We report here the genome sequence of an ancient human. Obtained from ∼4,000-year-old permafrost-preserved hair, the genome represents a male individual from the first known culture to settle in Greenland. Sequenced to an average depth of 20×, we recover 79% of the diploid genome, an amount close to the practical limit of current sequencing technologies. We identify 353,151 high-confidence single-nucleotide polymorphisms (SNPs), of which 6.8% hav…
Integrating large language models into prostate cancer training
BACKGROUND: Large language models (LLMs) are increasingly explored as tools for medical education. However, evidence remains limited regarding their pedagogical quality and real-world utility in prostate cancer teaching within urology residency training. METHODS: We conducted a two-phase study. Phase 1 benchmarked three LLMs (ChatGPT-4o, DeepSeek R1, and Gemini 2.0) on a structured 40-item prostate cancer education question bank using standardize…
Ancient DNA (1 work) · Artificial Intelligence in Healthcare and Education (1 work) · Benchmarking (1 work) · Biology (1 work) · Clinical Reasoning and Diagnostic Skills (1 work) · Curriculum (1 work) · Demography (1 work) · Educational measurement (1 work) · Evolutionary biology (1 work) · Forensic and Genetic Research (1 work)