现有TCR数据集,每条TCR有对应抗原的标签。为实现对TCR抗原特异性二分类预测,使用Keras库来搭建CNN模型,并利用Tokenzier将CDR3序列转化为数字序列,再利用pad_sequences将数字序列填充到相同长度。然后将标签转化为one-hot编码,并划分训练集和测试集。使用fit函数来训练模型,并使用acc、AUPRC和AUROC等评价指标对模型进行评估。python代码怎么写?
时间: 2024-02-19 15:58:42 浏览: 35
以下是一个简单的示例代码,用于搭建CNN模型并进行TCR抗原特异性二分类预测:
```python
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv1D, MaxPooling1D
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, average_precision_score
# 加载TCR数据集和标签
tcr_data = np.load("tcr_data.npy")
antigen_labels = np.load("antigen_labels.npy")
# 将CDR3序列数字化
tokenizer = Tokenizer(num_words=20, char_level=True)
tokenizer.fit_on_texts(tcr_data)
sequences = tokenizer.texts_to_sequences(tcr_data)
max_length = max([len(seq) for seq in sequences])
padded_sequences = pad_sequences(sequences, maxlen=max_length, padding='post')
# 将标签转化为one-hot编码
num_classes = len(np.unique(antigen_labels))
one_hot_labels = np.eye(num_classes)[antigen_labels]
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(padded_sequences, one_hot_labels, test_size=0.2, random_state=42)
# 搭建CNN模型
model = Sequential()
model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(max_length, 1)))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
# 编译模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# 训练模型
model.fit(X_train.reshape(-1, max_length, 1), y_train, batch_size=32, epochs=10, validation_data=(X_test.reshape(-1, max_length, 1), y_test))
# 评估模型
y_pred = model.predict(X_test)
y_pred_classes = np.argmax(y_pred, axis=1)
y_test_classes = np.argmax(y_test, axis=1)
acc = accuracy_score(y_test_classes, y_pred_classes)
precision = precision_score(y_test_classes, y_pred_classes, average='macro')
recall = recall_score(y_test_classes, y_pred_classes, average='macro')
f1 = f1_score(y_test_classes, y_pred_classes, average='macro')
auc = roc_auc_score(y_test, y_pred, multi_class='ovr')
auprc = average_precision_score(y_test, y_pred, average='macro')
print("Accuracy: {:.4f}".format(acc))
print("Precision: {:.4f}".format(precision))
print("Recall: {:.4f}".format(recall))
print("F1 score: {:.4f}".format(f1))
print("AUC: {:.4f}".format(auc))
print("AUPRC: {:.4f}".format(auprc))
```
在这个示例代码中,我们首先加载TCR数据集和标签,并使用Tokenizer将CDR3序列转化为数字序列,然后使用pad_sequences将数字序列填充到相同长度。接着,我们将标签转化为one-hot编码,并使用train_test_split划分训练集和测试集。
之后,我们搭建了一个简单的CNN模型,包括一个Conv1D层、一个MaxPooling1D层、一个Flatten层、两个Dense层和一个Dropout层。我们使用categorical_crossentropy作为损失函数,使用adam作为优化器,并使用accuracy作为评价指标。
最后,我们使用fit函数训练模型,并使用accuracy、precision、recall、f1、auc和auprc等评价指标对模型进行评估。