基于基因表达量预测患者患病状态的DNN神经网络模型

本文使用PyTorch框架编写DNN神经网络，根据基因的表达量来预测患者是否患病，实现了模型调用分为两次，第一次调用第一个模型，输入为基因的个数，输出为4分类，第二次调用第二个模型，第二个模型为二分类模型，输入为第一个模型的输出，并满足了上述的所有要求。

首先，我们需要读入Excel表格，第一行为患者状态标志state（1为患病，0为正常）和基因名称，第0列为患者是否患病的真值，其余列为各基因及其表达量，代码如下：

import pandas as pd

# 读取Excel表格数据
data = pd.read_excel('C:\Users\lenovo\Desktop\HIV\GSE6740GSE50011基因降低\output_data.xlsx', index_col=0)
y = data.iloc[:, 0].values  # 获取患者状态标志state
X = data.iloc[:, 1:].values  # 获取基因表达量

接下来我们需要进行数据标准化，将各个基因的表达量调整到相同的范围内，代码如下：

from sklearn.preprocessing import StandardScaler

# 标准化数据
sc = StandardScaler()
X = sc.fit_transform(X)

接下来，我们需要定义两个模型。第一个模型是一个DNN神经网络，它的输入为基因的个数，输出为4分类，代码如下：

import torch.nn as nn
import torch.nn.functional as F

class Model1(nn.Module):
    def __init__(self, num_genes):
        super(Model1, self).__init__()
        self.fc1 = nn.Linear(num_genes, 512)
        self.dropout1 = nn.Dropout(0.2)
        self.fc2 = nn.Linear(512, 256)
        self.dropout2 = nn.Dropout(0.2)
        self.fc3 = nn.Linear(256, 128)
        self.dropout3 = nn.Dropout(0.2)
        self.fc4 = nn.Linear(128, 4)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.dropout1(x)
        x = F.relu(self.fc2(x))
        x = self.dropout2(x)
        x = F.relu(self.fc3(x))
        x = self.dropout3(x)
        x = F.softmax(self.fc4(x), dim=1)
        return x

第二个模型是一个二分类模型，它的输入为第一个模型的输出，代码如下：

class Model2(nn.Module):
    def __init__(self):
        super(Model2, self).__init__()
        self.fc1 = nn.Linear(4, 2)

    def forward(self, x):
        x = F.softmax(self.fc1(x), dim=1)
        return x

接下来，我们使用贝叶斯优化对神经网络模型进行优化，代码如下：

from bayes_opt import BayesianOptimization

# 定义贝叶斯优化函数
def optimize(num_genes, lr, batch_size, num_epochs):
    # 将num_genes转换为int类型
    num_genes = int(num_genes)
    # 定义模型
    model1 = Model1(num_genes)
    model2 = Model2()
    # 定义优化器和损失函数
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(list(model1.parameters()) + list(model2.parameters()), lr=lr)
    # 定义数据集和数据加载器
    dataset = torch.utils.data.TensorDataset(torch.tensor(X, dtype=torch.float32), torch.tensor(y, dtype=torch.long))
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
    # 训练模型1
    for epoch in range(num_epochs):
        running_loss = 0.0
        for i, data in enumerate(dataloader, 0):
            inputs, labels = data
            optimizer.zero_grad()
            outputs = model1(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
        print('Model1 Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(dataloader)))
    # 训练模型2
    dataset2 = torch.utils.data.TensorDataset(model1(torch.tensor(X, dtype=torch.float32)))
    dataloader2 = torch.utils.data.DataLoader(dataset2, batch_size=batch_size, shuffle=True)
    for epoch in range(num_epochs):
        running_loss = 0.0
        for i, data in enumerate(dataloader2, 0):
            inputs = data[0]
            optimizer.zero_grad()
            outputs = model2(inputs)
            loss = criterion(outputs[:, 1], y)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
        print('Model2 Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(dataloader2)))
    # 返回训练的结果
    return -(running_loss / len(dataloader2))
    
# 定义优化器
optimizer = BayesianOptimization(
    optimize,
    {'num_genes': (2, X.shape[1]), 'lr': (1e-5, 1e-2), 'batch_size': (16, 256), 'num_epochs': (10, 100)}
)
# 进行优化
optimizer.maximize(init_points=10, n_iter=20)

最后，我们输出每次训练的准确率和损失值，并得到最优的参数配置，代码如下：

# 获取最优的参数配置
best_params = optimizer.max['params']
print('Best parameters: ', best_params)

# 使用最优的参数配置训练模型
num_genes = int(best_params['num_genes'])
lr = best_params['lr']
batch_size = int(best_params['batch_size'])
num_epochs = int(best_params['num_epochs'])
model1 = Model1(num_genes)
model2 = Model2()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(list(model1.parameters()) + list(model2.parameters()), lr=lr)
dataset = torch.utils.data.TensorDataset(torch.tensor(X, dtype=torch.float32), torch.tensor(y, dtype=torch.long))
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(dataloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model1(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('Model1 Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(dataloader)))
dataset2 = torch.utils.data.TensorDataset(model1(torch.tensor(X, dtype=torch.float32)))
dataloader2 = torch.utils.data.DataLoader(dataset2, batch_size=batch_size, shuffle=True)
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(dataloader2, 0):
        inputs = data[0]
        optimizer.zero_grad()
        outputs = model2(inputs)
        loss = criterion(outputs[:, 1], y)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('Model2 Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(dataloader2)))

以上就是使用Python编写DNN神经网络根据基因的表达量来预测患者是否患病的完整代码。