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

bio-workflows-biomarker-pipeline

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

Biomarker discovery: omics → feature selection → validation → report.

OpenClawNanoClaw分析处理复现实验bio-workflows-biomarker-pipeline🧠 bioos extended suitebioos extended bioinformatics suitebiomarker

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-workflows-biomarker-pipeline

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

Complete pipeline ，面向 expression data to validated biomarker panels ，支持 classifier。
X_train，X_test，y_train，y_test = train_test_split( X，y，test_size=0.2，stratify=y，random_state=42 )。

原始文档

SKILL.md 摘录

Step 1: Data Preparation

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

expr = pd.read_csv('expression.csv', index_col=0)
meta = pd.read_csv('metadata.csv', index_col=0)

X = expr.T  # samples x genes
y = meta.loc[X.index, 'condition'].values

## Fit scaler on training only to prevent data leakage

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

QC Checkpoint 1: Check class balance, sample counts per group

Minimum 10 samples per class recommended
Classes should be reasonably balanced (ratio <3:1)

Option A: Boruta (All-Relevant Selection)

from boruta import BorutaPy
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif

适用场景

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