随机森林在生物信息学中的应用：处理序列数据、蛋白质结构预测和基因预测

    import numpy as np
    from sklearn.ensemble import RandomForestClassifier
    
    X = [[0, 1], [1, 1], [2, 0]]   # input data (4 samples) with two features and class labels {0, 1} for binary classification
    y = [0, 1, 1]                    # output label of each sample
    
    rfc = RandomForestClassifier(n_estimators=100, random_state=42)    # initialize the Random Forest Classifier model with 100 trees and a seed value of 42
    rfc.fit(X, y)                                                         # fit the model on training data X and their corresponding output labels y
    
    pred_y = rfc.predict([[2., 1.]])                                       # make predictions using the trained model on new sample [[2., 1.]]
    print("Predicted Label:", pred_y[0])                                   # print predicted label
    
      
      
      
      
      
      
      
      
      
      
    

    import torch
    import torch.nn as nn
    import torch.optim as optim
    from torch.utils.data import DataLoader
    
    class Net(nn.Module):
    def __init__(self, in_channels=1, hidden_size=10, out_channels=1):
        super().__init__()
    
        self.conv1 = nn.Conv1d(in_channels, hidden_size, kernel_size=3, padding=1)
        self.relu = nn.ReLU()
        self.pooling = nn.MaxPool1d(kernel_size=2)
        self.lstm = nn.LSTM(hidden_size, hidden_size, num_layers=1, batch_first=True)
        self.out = nn.Linear(hidden_size, out_channels)
    
    def forward(self, x):
        x = self.conv1(x)                            # apply convolutional layer to get feature maps
        x = self.relu(x)                             # apply ReLU activation function to activate feature maps
        x = self.pooling(x)                          # perform max pooling operation on feature maps to reduce dimensions
    
        x = x.permute(0, 2, 1)                      # permute tensor to change shape from (batch size, seq length, feature dimension) to (batch size, feature dimension, seq length)
        _, h_n = self.lstm(x)                       # pass sequence through LSTM network to obtain hidden states for all time steps
        last_hidden = h_n[-1].squeeze().unsqueeze(0)     # extract final hidden state of LSTM network for all layers and remove extra dimension
        output = self.out(last_hidden)               # compute prediction score by applying linear transformation on extracted last hidden state of LSTM network
    
        return output
    
    net = Net()                                         # create neural network instance
    criterion = nn.MSELoss()                            # define loss function (mean squared error)
    optimizer = optim.Adam(net.parameters(), lr=0.01)   # define optimizer (stochastic gradient descent with momentum)
    
    for epoch in range(num_epochs):                     # train the model over multiple epochs
    running_loss = 0.0
    for i, data in enumerate(dataloader, 0):        # iterate over batches of training data
        inputs, labels = data                        # unpack batch data into inputs and labels
    
        optimizer.zero_grad()                         # zero gradients before updating weights
        outputs = net(inputs)                         # feed inputs to the network and get outputs
        loss = criterion(outputs, labels)             # calculate loss between predicted values and actual labels
        loss.backward()                               # backpropagate errors to update weight parameters
        optimizer.step()                              # update weight parameters based on computed gradients
    
        running_loss += loss.item()                  # accumulate loss over entire mini-batch
    
    if epoch % display_epoch == 0:                   # display current performance after every few epochs
        print('Epoch [%d/%d], Loss: %.6f' %
              (epoch+1, num_epochs, running_loss/len(trainloader)))
    
    test_outputs = []                                      # store test predictions
    with torch.no_grad():                                 # disable gradient calculation while testing to improve efficiency
    for i, data in enumerate(testloader, 0):           # iterate over batches of test data
        inputs, labels = data                           # unpack batch data into inputs and labels
        outputs = net(inputs)                            # feed inputs to the network and get outputs
        test_outputs.append(outputs.cpu().numpy())      # append predicted values to list for computing metrics later
    
    # compute various evaluation metrics such as mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R^2), etc.
    # use appropriate metric functions or libraries depending on your choice of machine learning framework or library 
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    import pandas as pd
    import numpy as np
    import scipy.stats as stats
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.metrics import accuracy_score
    
    np.random.seed(42)  # set random seed for reproducibility
    
    # read data into Pandas DataFrame
    df = pd.read_csv('sequence_data.csv')
    
    # preprocess data - drop any missing values or unnecessary columns
    df = df.dropna()  # drop any rows with missing values
    df = df.drop(['id','sequence'], axis=1)  # drop ID and sequence column
    
    # split dataset into training and testing sets (70% vs. 30%)
    split_idx = int(0.7*len(df))
    train_data = df[:split_idx]
    test_data = df[split_idx:]
    
    # convert categorical variables into one-hot encoded vectors using dummy encoding
    dummy_encoded_columns = ['species']
    train_data = pd.get_dummies(train_data, columns=dummy_encoded_columns)
    test_data = pd.get_dummies(test_data, columns=dummy_encoded_columns)
    
    # separate features and labels
    train_features = train_data.iloc[:, :-1].values
    train_labels = train_data.iloc[:, -1].values
    test_features = test_data.iloc[:, :-1].values
    test_labels = test_data.iloc[:, -1].values
    
    # normalize features using min-max scaling
    min_max_scaler = MinMaxScaler()
    train_features = min_max_scaler.fit_transform(train_features)
    test_features = min_max_scaler.transform(test_features)
    
    # initialize Bayesian random forest classifier with 100 trees and tree depth of 5
    brf = RandomForestClassifier(n_estimators=100, max_depth=5)
    
    # train the model on the training data
    brf.fit(train_features, train_labels)
    
    # predict on the test data and evaluate accuracy
    predicted_labels = brf.predict(test_features)
    accuracy = accuracy_score(test_labels, predicted_labels)