给我breast cancer cell dataset
时间: 2023-12-03 11:04:34 浏览: 66
由于不清楚你需要哪个具体的数据集,以下提供两个常用的乳腺癌细胞数据集供参考:
1. 威斯康星大学乳腺癌数据集(Wisconsin Breast Cancer Dataset)
该数据集包含569个样本,其中212个是恶性肿瘤,357个是良性肿瘤。每个样本都有30个特征,包括肿瘤大小、均匀度、边缘光滑度等。这个数据集可以用于分类任务。
数据集链接:https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
2. 癌症基因组图谱数据集(The Cancer Genome Atlas (TCGA))
该数据集包含了一系列癌症类型的基因组图谱数据,包括乳腺癌。数据集提供了丰富的信息,包括基因表达、基因突变、临床信息等。这个数据集可以用于生物信息学分析和机器学习任务。
数据集链接:https://portal.gdc.cancer.gov/
相关问题
给出SVM 回归 with Breast Cancer Dataset的Python代码
以下是使用SVM回归算法对Breast Cancer数据集进行预测的Python代码:
```python
# 导入必要的库
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error
# 加载Breast Cancer数据集
breast_cancer = datasets.load_breast_cancer()
# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(breast_cancer.data, breast_cancer.target, test_size=.2, random_state=42)
# 创建SVM回归模型
svm_reg = SVR(kernel='linear', C=1, epsilon=.1)
# 训练模型
svm_reg.fit(X_train, y_train)
# 预测测试集
y_pred = svm_reg.predict(X_test)
# 计算均方误差
mse = mean_squared_error(y_test, y_pred)
print("均方误差:", mse)
```
以上代码使用线性核函数创建SVM回归模型,并使用Breast Cancer数据集进行训练和测试。最后,计算预测结果与真实结果之间的均方误差。
Harmonising ER+ Breast cancer
"Harmonising ER+ breast cancer" refers to the process of standardizing the way estrogen receptor positive (ER+) breast cancer is classified and treated. ER+ breast cancer is a type of breast cancer that tests positive for the presence of estrogen receptors, which promote the growth of cancer cells. Harmonizing ER+ breast cancer involves ensuring that healthcare providers use consistent criteria for diagnosing and staging the cancer, as well as providing standardized treatment options based on the stage and characteristics of the cancer. This can lead to better outcomes for patients and more efficient use of healthcare resources.
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