数据与复现生物信息与基因组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-machine-learning-survival-analysis

维护者 FreedomIntelligence · 最近更新 2026年4月1日

Survival ML: RSF, DeepSurv, CoxBoost from omics features.

OpenClawNanoClaw分析处理复现实验bio-machine-learning-survival-analysis🧠 bioos extended suitebioos extended bioinformatics suitesurvival

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-machine-learning-survival-analysis

维护者: FreedomIntelligence
许可: MIT
最近更新: 2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

kmf.fit(T，event_observed=E)。
kmf.plot_survival_function() plt.xlabel('Time (months)') plt.ylabel('Survival probability') plt.savefig('km_curve.png'，dpi=150)。

原始文档

SKILL.md 摘录

Kaplan-Meier Curves

from lifelines import KaplanMeierFitter
import matplotlib.pyplot as plt

kmf = KaplanMeierFitter()

## Compare Groups with Log-Rank Test

```python
from lifelines import KaplanMeierFitter
from lifelines.statistics import logrank_test
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(8, 6))

for group, color in zip(['high', 'low'], ['red', 'blue']):
    mask = df['risk_group'] == group
    kmf = KaplanMeierFitter()
    kmf.fit(df.loc[mask, 'time'], event_observed=df.loc[mask, 'event'], label=group)
    kmf.plot_survival_function(ax=ax, color=color)

## Log-rank test

high = df[df['risk_group'] == 'high']
low = df[df['risk_group'] == 'low']
results = logrank_test(high['time'], low['time'], event_observed_A=high['event'], event_observed_B=low['event'])
print(f'Log-rank p-value: {results.p_value:.4e}')

ax.set_xlabel('Time (months)')
ax.set_ylabel('Survival probability')
ax.set_title(f'Log-rank p = {results.p_value:.4e}')
plt.savefig('km_comparison.png', dpi=150)

适用场景

Use bio-machine-learning-survival-analysis ，用于 genomics 、 bioinformatics workflows。
Apply bio-machine-learning-survival-analysis to sequencing，variant，或 omics analysis tasks。