示例

示例1：完整的衰老检测流程

Python

from deepome import DeepoMeClient

client = DeepoMeClient(api_key="YOUR_API_KEY")

# Step 1: 创建检测订单
order = client.samples.create(
    user_id="USER_001",
    sample_type="saliva",
    test_items=["epigenetic_age", "organ_age", "pathway"]
)

# Step 2: 等待检测完成（通常5-7个工作日）
import time
while True:
    status = client.samples.get_status(order.sample_id)
    if status.status == "completed":
        break
    time.sleep(3600)  # 每小时检查一次

# Step 3: 获取完整报告
report = client.samples.get_report(order.sample_id)

# Step 4: 获取药物推荐
screening = client.screening.create(
    sample_id=order.sample_id,
    categories=["aging_intervention", "metabolic"]
)

# Step 5: 输出结果
print(f"=== DeepoMe 衰老检测报告 ===")
print(f"日历年龄: {report.chronological_age}")
print(f"生物年龄: {report.biological_age}")
print(f"年龄差异: {report.biological_age - report.chronological_age:+.1f} 岁")
print()
print("器官衰老评估:")
for organ in report.organ_ages:
    status = "正常" if abs(organ.offset) <= 2 else "注意"
    print(f"  {organ.name}: {organ.age} 岁 ({status})")
print()
print("推荐药物 TOP 5:")
for drug in screening.candidates[:5]:
    print(f"  {drug.name} - 匹配度: {drug.match_score:.1%}")

示例2：批量通路分析

sample_ids = ["CAP-2024-001", "CAP-2024-002", "CAP-2024-003"]

results = client.moa.batch_pathway_analysis(
    sample_ids=sample_ids,
    pathways=["mTOR", "AMPK", "NF_KB", "SIRT1"]
)

for result in results:
    print(f"\n样本 {result.sample_id}:")
    for pathway, score in result.pathway_scores.items():
        print(f"  {pathway}: 活性={score.activity}, 偏移={score.aging_offset:+.1f}")

示例3：Webhook集成

# 注册Webhook接收检测完成通知
client.webhooks.create(
    url="https://your-app.com/api/webhooks/deepome",
    events=["sample.completed", "screening.completed"]
)

参考资料

1. DeepoMe API 完整文档
2. SDK API 参考