原文核对：2026-10-13 · 中文正文 · 英文提示词与演示
场景定位
标准化实体，并行收集遗传、队列、临床、文献和表达证据，分别评分，再形成排名和可复用图表。
- 难度
- 高级
- 时间跨度
- 长时间运行
适合用于
- 需要多类证据支持的靶点优先级问题。
- 希望并行科学研究后综合结论的团队。
- 需要原始响应、明确评分规则和可复用视觉材料的科学家。
相关工具
搜索科学数据库和文献，为通路、转化、可成药性及竞争证据提供依据。
Life Science Research ↗起步提示词
英文原文 · 可复制后按实际材料修改
Use the Life Science Research plugin to compare TSLP, IL33, and IL1RL1 for asthma target prioritization.
Run these independent lanes in parallel with subagents:
- Human genetics and GWAS: gwas-catalog-skill, opentargets-skill, gnomad-graphql-skill
- Cohort replication and PheWAS: finngen-phewas-skill, ukb-topmed-phewas-skill, biobankjapan-phewas-skill, tpmi-phewas-skill
- Target-disease evidence and disease context: opentargets-skill, efo-ontology-skill
- Clinical and regulatory precedent: clinicaltrials-skill, opentargets-skill, chembl-skill, pharmgkb-skill
- Literature and public-dataset context: ncbi-entrez-skill, ncbi-pmc-skill, biorxiv-skill, ncbi-datasets-skill, biostudies-arrayexpress-skill
- Expression and tissue/cell-type context: human-protein-atlas-skill, gtex-eqtl-skill, cellxgene-skill, bgee-skill
For each lane:
- score TSLP, IL33, IL1RL1 on a 1-5 scale
- keep direct asthma evidence separate from adjacent allergic/atopic phenotypes
- save raw payloads when helpful
Then synthesize:
- a lane-by-target score table
- a final rank of TSLP, IL33, IL1RL1
- a confidence assessment and the main caveats
- two visuals: a prioritization heatmap and a GWAS summary figure with the lead asthma-linked variants for each target使用技能
Life Science Research 为各证据方向提供技能：
- 人类遗传与 GWAS：gwas-catalog-skill、opentargets-skill、gnomad-graphql-skill。
- 队列复现与 PheWAS：finngen-phewas-skill、ukb-topmed-phewas-skill、biobankjapan-phewas-skill、tpmi-phewas-skill。
- 靶点疾病关联及背景：opentargets-skill、efo-ontology-skill。
- 临床与监管先例：clinicaltrials-skill、opentargets-skill、chembl-skill、pharmgkb-skill。
- 文献与公开数据集：ncbi-entrez-skill、ncbi-pmc-skill、biorxiv-skill、ncbi-datasets-skill、biostudies-arrayexpress-skill。
- 表达与组织/细胞类型：human-protein-atlas-skill、gtex-eqtl-skill、cellxgene-skill、bgee-skill。
可以明确提及技能，或让 ChatGPT 选择调用时机。
操作步骤
- 给出具体比较问题、准确靶点、疾病和证据方向。
- 调用 Life Science Research，要求子代理并行处理，各自保持明确范围。
- 每条证据方向按固定 1–5 分评分，区分直接疾病证据与相邻表型。
- 在同一对话中审阅保存的原始响应、证据方向×靶点评分表和综合排名。
展开英文原文对照
Use ChatGPT with the Life Science Research plugin to normalize entities, retrieve genetics, cohort, clinical, literature, and expression evidence in parallel, score each lane, and produce a final ranking with reusable visuals.
Leverage skills
The Life Science Research plugin includes skills for each evidence lane:
- Human genetics and GWAS: gwas-catalog-skill, opentargets-skill, gnomad-graphql-skill
- Cohort replication and PheWAS: finngen-phewas-skill, ukb-topmed-phewas-skill, biobankjapan-phewas-skill, tpmi-phewas-skill
- Target-disease evidence and disease context: opentargets-skill, efo-ontology-skill
- Clinical and regulatory precedent: clinicaltrials-skill, opentargets-skill, chembl-skill, pharmgkb-skill
- Literature and public-dataset context: ncbi-entrez-skill, ncbi-pmc-skill, biorxiv-skill, ncbi-datasets-skill, biostudies-arrayexpress-skill
- Expression and tissue/cell-type context: human-protein-atlas-skill, gtex-eqtl-skill, cellxgene-skill, bgee-skill
Use these skills by mentioning them specifically, or let ChatGPT decide when to use them.
Step-by-step guide
- Start with a concrete comparison question and the exact targets, disease, and evidence lanes you want ChatGPT to cover.
- Invoke the Life Science Research plugin and tell ChatGPT to run the lanes in parallel with subagents so each evidence family stays bounded.
- Ask ChatGPT to score each lane on a fixed 1-5 scale and to keep direct disease evidence separate from adjacent phenotypes.
- Review the saved raw payloads, the lane-by-target score table, and the synthesized rank in the same chat.