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-biomarker-discovery

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

Identify biomarkers from omics data with LASSO, elastic net, SHAP.

OpenClawNanoClaw分析处理复现实验bio-machine-learning-biomarker-discovery🧠 bioos extended suitebioos extended bioinformatics suiteidentify

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

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

boruta = BorutaPy(rf，n_estimators='auto'，max_iter=100，random_state=42，verbose=0) boruta.fit(X.values，y)。
selected = X.columns[boruta.support_] tentative = X.columns[boruta.support_weak_] print(f'Selected：{len(selected)}，Tentative：{len(tentative)}')。
feature_ranks = pd.DataFrame({ 'feature'：X.columns，'rank'：boruta.ranking_，'selected'：boruta.support_ }).sort_values('rank')。
selected_features = mrmr_classif(X=X，y=pd.Series(y)，K=50) X_selected = X[selected_features]。

原始文档

SKILL.md 摘录

Boruta All-Relevant Selection

Identifies all features that are significantly better than random (shadow features).

from boruta import BorutaPy
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
import numpy as np

rf = RandomForestClassifier(n_estimators=100, n_jobs=-1, random_state=42)

## mRMR (Minimum Redundancy Maximum Relevance)

Selects features that are individually relevant but minimally redundant with each other.

```python
from mrmr import mrmr_classif

## LASSO Feature Selection

L1 regularization drives irrelevant coefficients to zero.

```python
from sklearn.linear_model import LassoCV
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

适用场景

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